CDC Daily by State
davegoblue
2022-09-07
Background
This file is designed to use CDC data to assess coronavirus disease burden by state, including creating and analyzing state-level clusters.
Through March 7, 2021, The COVID Tracking Project collected and integrated data on tests, cases, hospitalizations, deaths, and the like by state and date. The latest code for using this data is available in Coronavirus_Statistics_CTP_v004.Rmd.
The COVID Tracking Project suggest that US federal data sources are now sufficiently robust to be used for analyses that previously relied on COVID Tracking Project. This code is an attempt to update modules in Coronavirus_Statistics_CTP_v004.Rmd to leverage US federal data.
The code in this module builds on code available in _v004, with function and mapping files updated:
- Generic_Added_Utility_Functions_202105_v001.R - generic functions
that can be used in other areas
- Coronavirus_CDC_Daily_Functions_v001.R - functions specific to coronavirus daily data
Broadly, the CDC data analyzed by this module includes:
- CDC case and death data by state and date are available for download
on the CDC
website
- CDC hospital data are available for download on the healthdata.gov
website
- CDC vaccines data are also available for download on the CDC website:
Functions and Mapping Files
The tidyverse package is loaded and functions are sourced:
# The tidyverse functions are routinely used without package::function format
library(tidyverse)## ── Attaching packages ─────────────────────────────────────── tidyverse 1.3.2 ──
## ✔ ggplot2 3.3.6 ✔ purrr 0.3.4
## ✔ tibble 3.1.8 ✔ dplyr 1.0.9
## ✔ tidyr 1.2.0 ✔ stringr 1.4.0
## ✔ readr 2.1.2 ✔ forcats 0.5.1
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag() masks stats::lag()library(geofacet)
# Functions are available in source file
source("./Generic_Added_Utility_Functions_202105_v001.R")
source("./Coronavirus_CDC_Daily_Functions_v002.R")A series of mapping files are also available to allow for parameterized processing. Mappings include:
- urlMapper - mapping file for urlType and url location to download
data
- renMapper - mapping file for renaming of variables in the raw data
file
- selfListMapper - mapping file for transformations by variable
type
- fullListMapper - mapping file for transformations across variable
types
- lstComboMapper - mapping file for elements to be combined by data
type (most common is to combine NYC and NYS data to NY if the file
provides them separately)
- uqMapper - mapping file for fields that should combine to be unique
keys for processed files
- lstFilterMapper - mapping file for filtering to subset (most common
is to keep 50 states and DC)
- vecSelectMapper - mapping file for variables to keep
- checkControlGroupMapper - mapping file for group_by() of control
total checks
- checkControlAggMapper - mapping file for numeric variables for
control total checks
- checkSimilarityMapper - mapping file for similarity checks to
perform
- plotSimilarityMaooer - mapping file for fields where differences in universe should be plotted
- keyAggMapper - mapping file for the aggregate-level control total
checks to perform
- perCapMapper - named vector that drives conversion from original
field name to per capita field name
- hhsMapper - named vector that drivers numerical variables to keep (and renaming) from HHS capacity data file
These default parameters are maintained in a separate .R file and can be sourced:
source("./Coronavirus_CDC_Daily_Default_Mappings_v002.R")Example Code Processing
The function is run to download and process the latest CDC case, hospitalization, and death data:
readList <- list("cdcDaily"="./RInputFiles/Coronavirus/CDC_dc_downloaded_220907.csv",
"cdcHosp"="./RInputFiles/Coronavirus/CDC_h_downloaded_220907.csv",
"vax"="./RInputFiles/Coronavirus/vaxData_downloaded_220907.csv"
)
compareList <- list("cdcDaily"=readFromRDS("cdc_daily_220805")$dfRaw$cdcDaily,
"cdcHosp"=readFromRDS("cdc_daily_220805")$dfRaw$cdcHosp,
"vax"=readFromRDS("cdc_daily_220805")$dfRaw$vax
)
cdc_daily_220907 <- readRunCDCDaily(thruLabel="Sep 05, 2022",
downloadTo=lapply(readList, FUN=function(x) if(file.exists(x)) NA else x),
readFrom=readList,
compareFile=compareList,
writeLog=NULL,
useClusters=readFromRDS("cdc_daily_210528")$useClusters,
weightedMeanAggs=c("tcpm7", "tdpm7", "cpm7", "dpm7", "hpm7",
"vxcpm7", "vxcgte65pct"
),
skipAssessmentPlots=FALSE,
brewPalette="Paired"
)## Rows: 57480 Columns: 15
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (5): submission_date, state, created_at, consent_cases, consent_deaths
## dbl (10): tot_cases, conf_cases, prob_cases, new_case, pnew_case, tot_death,...
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.##
## *** File has been checked for uniqueness by: state date
##
##
## Checking for similarity of: column names
## In reference but not in current:
## In current but not in reference:
##
## Checking for similarity of: date
## In reference but not in current: 0
## In current but not in reference: 33
##
## Checking for similarity of: state
## In reference but not in current:
## In current but not in reference:
##
##
## ***Differences of at least 5 and at least 5%
##
## date name newValue refValue absDelta pctDelta
## 1 2022-07-31 new_deaths 116 23 93 1.33812950
## 2 2022-07-30 new_deaths 130 34 96 1.17073171
## 3 2022-07-23 new_deaths 158 109 49 0.36704120
## 4 2022-07-24 new_deaths 170 126 44 0.29729730
## 5 2022-08-01 new_deaths 433 347 86 0.22051282
## 6 2022-07-28 new_deaths 518 434 84 0.17647059
## 7 2022-07-16 new_deaths 151 127 24 0.17266187
## 8 2022-07-29 new_deaths 639 543 96 0.16243655
## 9 2022-07-25 new_deaths 306 265 41 0.14360771
## 10 2022-08-03 new_deaths 716 632 84 0.12462908
## 11 2022-08-02 new_deaths 715 632 83 0.12323682
## 12 2022-07-27 new_deaths 703 634 69 0.10321616
## 13 2022-07-10 new_deaths 114 103 11 0.10138249
## 14 2022-07-22 new_deaths 628 580 48 0.07947020
## 15 2022-06-18 new_deaths 105 97 8 0.07920792
## 16 2022-07-26 new_deaths 643 596 47 0.07586764
## 17 2022-07-04 new_deaths 138 128 10 0.07518797
## 18 2022-07-18 new_deaths 361 337 24 0.06876791
## 19 2022-07-21 new_deaths 500 471 29 0.05973223
## 20 2022-07-09 new_deaths 110 104 6 0.05607477
## 21 2022-07-30 new_cases 38338 32823 5515 0.15500063
## 22 2022-07-31 new_cases 39803 35276 4527 0.12059298
## 23 2022-08-01 new_cases 126242 133013 6771 0.05223429
##
##
## ***Differences of at least 0 and at least 0.1%
##
## state name newValue refValue absDelta pctDelta
## 1 NC tot_deaths 11352019 11325521 26498 0.002336938
## 2 KY tot_deaths 6954858 6939424 15434 0.002221633
## 3 NC new_deaths 26101 25692 409 0.015793640
## 4 KY new_deaths 16647 16438 209 0.012634124
## 5 FL new_deaths 78609 77823 786 0.010049095
## 6 AL new_deaths 20081 19974 107 0.005342654
## 7 SC new_deaths 18211 18192 19 0.001043870
## 8 SC new_cases 1626423 1605165 21258 0.013156380
## 9 KY new_cases 1489715 1479668 10047 0.006767062
## 10 NC new_cases 3026839 3022204 4635 0.001532474
##
##
##
## Raw file for cdcDaily:
## Rows: 57,480
## Columns: 15
## $ date <date> 2021-03-11, 2021-12-01, 2022-01-02, 2021-09-01, 2021-0…
## $ state <chr> "KS", "ND", "AS", "ND", "IN", "FL", "TN", "PR", "PW", "…
## $ tot_cases <dbl> 297229, 163565, 11, 118491, 668765, 3510205, 64885, 173…
## $ conf_cases <dbl> 241035, 135705, NA, 107475, NA, NA, 64371, 144788, NA, …
## $ prob_cases <dbl> 56194, 27860, NA, 11016, NA, NA, 514, 29179, NA, NA, NA…
## $ new_cases <dbl> 0, 589, 0, 536, 487, 9979, 1816, 667, 0, 317, 0, 28, 8,…
## $ pnew_case <dbl> 0, 220, 0, 66, 0, 2709, 30, 274, 0, 0, 0, 5, 0, 46, 70,…
## $ tot_deaths <dbl> 4851, 1907, 0, 1562, 12710, 56036, 749, 2911, 0, 561, 0…
## $ conf_death <dbl> NA, NA, NA, NA, 12315, NA, 722, 2482, NA, NA, NA, 1601,…
## $ prob_death <dbl> NA, NA, NA, NA, 395, NA, 27, 429, NA, NA, NA, 366, NA, …
## $ new_deaths <dbl> 0, 9, 0, 1, 7, 294, 8, 8, 0, 12, 0, 0, 0, 5, 0, 4, 0, 0…
## $ pnew_death <dbl> 0, 0, 0, 0, 2, 26, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ created_at <chr> "03/12/2021 03:20:13 PM", "12/02/2021 02:35:20 PM", "01…
## $ consent_cases <chr> "Agree", "Agree", NA, "Agree", "Not agree", "Not agree"…
## $ consent_deaths <chr> "N/A", "Not agree", NA, "Not agree", "Agree", "Not agre…## Rows: 49367 Columns: 135
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (1): state
## dbl (132): critical_staffing_shortage_today_yes, critical_staffing_shortage...
## lgl (1): geocoded_state
## date (1): date
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
##
## *** File has been checked for uniqueness by: state date
##
##
## Checking for similarity of: column names
## In reference but not in current:
## In current but not in reference:
##
## Checking for similarity of: date
## In reference but not in current: 0
## In current but not in reference: 33
##
## Checking for similarity of: state
## In reference but not in current:
## In current but not in reference:
##
##
## ***Differences of at least 5 and at least 5%
##
## date name newValue refValue absDelta pctDelta
## 1 2020-07-25 hosp_ped 3964 4594 630 0.1472307
##
##
## ***Differences of at least 0 and at least 0.1%
##
## state name newValue refValue absDelta pctDelta
## 1 ND inp 122358 122070 288 0.002356522
## 2 NH hosp_ped 1127 1167 40 0.034873583
## 3 KS hosp_ped 4891 4725 166 0.034525790
## 4 ME hosp_ped 2387 2338 49 0.020740741
## 5 KY hosp_ped 20285 20665 380 0.018559219
## 6 WV hosp_ped 5686 5753 67 0.011714311
## 7 VA hosp_ped 18388 18192 196 0.010716238
## 8 TN hosp_ped 22215 22423 208 0.009319414
## 9 NM hosp_ped 8054 8114 60 0.007422068
## 10 SC hosp_ped 9035 9092 57 0.006288961
## 11 DE hosp_ped 5277 5310 33 0.006234061
## 12 NJ hosp_ped 19499 19618 119 0.006084311
## 13 UT hosp_ped 10271 10210 61 0.005956740
## 14 MS hosp_ped 11803 11854 51 0.004311620
## 15 AL hosp_ped 20947 21025 78 0.003716764
## 16 VT hosp_ped 540 542 2 0.003696858
## 17 WY hosp_ped 859 856 3 0.003498542
## 18 MA hosp_ped 12619 12657 38 0.003006805
## 19 NC hosp_ped 30541 30453 88 0.002885530
## 20 PR hosp_ped 23021 22959 62 0.002696825
## 21 IL hosp_ped 44084 44202 118 0.002673131
## 22 AK hosp_ped 2664 2657 7 0.002631084
## 23 MO hosp_ped 39841 39939 98 0.002456756
## 24 PA hosp_ped 55078 55211 133 0.002411845
## 25 AR hosp_ped 12767 12747 20 0.001567767
## 26 CO hosp_ped 22421 22387 34 0.001517586
## 27 OH hosp_ped 91382 91261 121 0.001324989
## 28 AZ hosp_ped 27563 27532 31 0.001125329
## 29 MD hosp_ped 17240 17221 19 0.001102696
## 30 ND hosp_adult 115920 115630 290 0.002504859
##
##
##
## Raw file for cdcHosp:
## Rows: 49,367
## Columns: 135
## $ state <chr> …
## $ date <date> …
## $ critical_staffing_shortage_today_yes <dbl> …
## $ critical_staffing_shortage_today_no <dbl> …
## $ critical_staffing_shortage_today_not_reported <dbl> …
## $ critical_staffing_shortage_anticipated_within_week_yes <dbl> …
## $ critical_staffing_shortage_anticipated_within_week_no <dbl> …
## $ critical_staffing_shortage_anticipated_within_week_not_reported <dbl> …
## $ hospital_onset_covid <dbl> …
## $ hospital_onset_covid_coverage <dbl> …
## $ inpatient_beds <dbl> …
## $ inpatient_beds_coverage <dbl> …
## $ inpatient_beds_used <dbl> …
## $ inpatient_beds_used_coverage <dbl> …
## $ inp <dbl> …
## $ inpatient_beds_used_covid_coverage <dbl> …
## $ previous_day_admission_adult_covid_confirmed <dbl> …
## $ previous_day_admission_adult_covid_confirmed_coverage <dbl> …
## $ previous_day_admission_adult_covid_suspected <dbl> …
## $ previous_day_admission_adult_covid_suspected_coverage <dbl> …
## $ previous_day_admission_pediatric_covid_confirmed <dbl> …
## $ previous_day_admission_pediatric_covid_confirmed_coverage <dbl> …
## $ previous_day_admission_pediatric_covid_suspected <dbl> …
## $ previous_day_admission_pediatric_covid_suspected_coverage <dbl> …
## $ staffed_adult_icu_bed_occupancy <dbl> …
## $ staffed_adult_icu_bed_occupancy_coverage <dbl> …
## $ staffed_icu_adult_patients_confirmed_and_suspected_covid <dbl> …
## $ staffed_icu_adult_patients_confirmed_and_suspected_covid_coverage <dbl> …
## $ staffed_icu_adult_patients_confirmed_covid <dbl> …
## $ staffed_icu_adult_patients_confirmed_covid_coverage <dbl> …
## $ hosp_adult <dbl> …
## $ total_adult_patients_hospitalized_confirmed_and_suspected_covid_coverage <dbl> …
## $ total_adult_patients_hospitalized_confirmed_covid <dbl> …
## $ total_adult_patients_hospitalized_confirmed_covid_coverage <dbl> …
## $ hosp_ped <dbl> …
## $ total_pediatric_patients_hospitalized_confirmed_and_suspected_covid_coverage <dbl> …
## $ total_pediatric_patients_hospitalized_confirmed_covid <dbl> …
## $ total_pediatric_patients_hospitalized_confirmed_covid_coverage <dbl> …
## $ total_staffed_adult_icu_beds <dbl> …
## $ total_staffed_adult_icu_beds_coverage <dbl> …
## $ inpatient_beds_utilization <dbl> …
## $ inpatient_beds_utilization_coverage <dbl> …
## $ inpatient_beds_utilization_numerator <dbl> …
## $ inpatient_beds_utilization_denominator <dbl> …
## $ percent_of_inpatients_with_covid <dbl> …
## $ percent_of_inpatients_with_covid_coverage <dbl> …
## $ percent_of_inpatients_with_covid_numerator <dbl> …
## $ percent_of_inpatients_with_covid_denominator <dbl> …
## $ inpatient_bed_covid_utilization <dbl> …
## $ inpatient_bed_covid_utilization_coverage <dbl> …
## $ inpatient_bed_covid_utilization_numerator <dbl> …
## $ inpatient_bed_covid_utilization_denominator <dbl> …
## $ adult_icu_bed_covid_utilization <dbl> …
## $ adult_icu_bed_covid_utilization_coverage <dbl> …
## $ adult_icu_bed_covid_utilization_numerator <dbl> …
## $ adult_icu_bed_covid_utilization_denominator <dbl> …
## $ adult_icu_bed_utilization <dbl> …
## $ adult_icu_bed_utilization_coverage <dbl> …
## $ adult_icu_bed_utilization_numerator <dbl> …
## $ adult_icu_bed_utilization_denominator <dbl> …
## $ geocoded_state <lgl> …
## $ `previous_day_admission_adult_covid_confirmed_18-19` <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_18-19_coverage` <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_20-29` <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_20-29_coverage` <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_30-39` <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_30-39_coverage` <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_40-49` <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_40-49_coverage` <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_50-59` <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_50-59_coverage` <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_60-69` <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_60-69_coverage` <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_70-79` <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_70-79_coverage` <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_80+` <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_80+_coverage` <dbl> …
## $ previous_day_admission_adult_covid_confirmed_unknown <dbl> …
## $ previous_day_admission_adult_covid_confirmed_unknown_coverage <dbl> …
## $ `previous_day_admission_adult_covid_suspected_18-19` <dbl> …
## $ `previous_day_admission_adult_covid_suspected_18-19_coverage` <dbl> …
## $ `previous_day_admission_adult_covid_suspected_20-29` <dbl> …
## $ `previous_day_admission_adult_covid_suspected_20-29_coverage` <dbl> …
## $ `previous_day_admission_adult_covid_suspected_30-39` <dbl> …
## $ `previous_day_admission_adult_covid_suspected_30-39_coverage` <dbl> …
## $ `previous_day_admission_adult_covid_suspected_40-49` <dbl> …
## $ `previous_day_admission_adult_covid_suspected_40-49_coverage` <dbl> …
## $ `previous_day_admission_adult_covid_suspected_50-59` <dbl> …
## $ `previous_day_admission_adult_covid_suspected_50-59_coverage` <dbl> …
## $ `previous_day_admission_adult_covid_suspected_60-69` <dbl> …
## $ `previous_day_admission_adult_covid_suspected_60-69_coverage` <dbl> …
## $ `previous_day_admission_adult_covid_suspected_70-79` <dbl> …
## $ `previous_day_admission_adult_covid_suspected_70-79_coverage` <dbl> …
## $ `previous_day_admission_adult_covid_suspected_80+` <dbl> …
## $ `previous_day_admission_adult_covid_suspected_80+_coverage` <dbl> …
## $ previous_day_admission_adult_covid_suspected_unknown <dbl> …
## $ previous_day_admission_adult_covid_suspected_unknown_coverage <dbl> …
## $ deaths_covid <dbl> …
## $ deaths_covid_coverage <dbl> …
## $ on_hand_supply_therapeutic_a_casirivimab_imdevimab_courses <dbl> …
## $ on_hand_supply_therapeutic_b_bamlanivimab_courses <dbl> …
## $ on_hand_supply_therapeutic_c_bamlanivimab_etesevimab_courses <dbl> …
## $ previous_week_therapeutic_a_casirivimab_imdevimab_courses_used <dbl> …
## $ previous_week_therapeutic_b_bamlanivimab_courses_used <dbl> …
## $ previous_week_therapeutic_c_bamlanivimab_etesevimab_courses_used <dbl> …
## $ icu_patients_confirmed_influenza <dbl> …
## $ icu_patients_confirmed_influenza_coverage <dbl> …
## $ previous_day_admission_influenza_confirmed <dbl> …
## $ previous_day_admission_influenza_confirmed_coverage <dbl> …
## $ previous_day_deaths_covid_and_influenza <dbl> …
## $ previous_day_deaths_covid_and_influenza_coverage <dbl> …
## $ previous_day_deaths_influenza <dbl> …
## $ previous_day_deaths_influenza_coverage <dbl> …
## $ total_patients_hospitalized_confirmed_influenza <dbl> …
## $ total_patients_hospitalized_confirmed_influenza_and_covid <dbl> …
## $ total_patients_hospitalized_confirmed_influenza_and_covid_coverage <dbl> …
## $ total_patients_hospitalized_confirmed_influenza_coverage <dbl> …
## $ all_pediatric_inpatient_bed_occupied <dbl> …
## $ all_pediatric_inpatient_bed_occupied_coverage <dbl> …
## $ all_pediatric_inpatient_beds <dbl> …
## $ all_pediatric_inpatient_beds_coverage <dbl> …
## $ previous_day_admission_pediatric_covid_confirmed_0_4 <dbl> …
## $ previous_day_admission_pediatric_covid_confirmed_0_4_coverage <dbl> …
## $ previous_day_admission_pediatric_covid_confirmed_12_17 <dbl> …
## $ previous_day_admission_pediatric_covid_confirmed_12_17_coverage <dbl> …
## $ previous_day_admission_pediatric_covid_confirmed_5_11 <dbl> …
## $ previous_day_admission_pediatric_covid_confirmed_5_11_coverage <dbl> …
## $ previous_day_admission_pediatric_covid_confirmed_unknown <dbl> …
## $ previous_day_admission_pediatric_covid_confirmed_unknown_coverage <dbl> …
## $ staffed_icu_pediatric_patients_confirmed_covid <dbl> …
## $ staffed_icu_pediatric_patients_confirmed_covid_coverage <dbl> …
## $ staffed_pediatric_icu_bed_occupancy <dbl> …
## $ staffed_pediatric_icu_bed_occupancy_coverage <dbl> …
## $ total_staffed_pediatric_icu_beds <dbl> …
## $ total_staffed_pediatric_icu_beds_coverage <dbl> …## Rows: 36184 Columns: 96
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (2): Date, Location
## dbl (94): MMWR_week, Distributed, Distributed_Janssen, Distributed_Moderna, ...
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
##
## *** File has been checked for uniqueness by: state date
##
##
## Checking for similarity of: column names
## In reference but not in current:
## In current but not in reference: Distributed_Novavax Administered_Novavax Series_Complete_Novavax
##
## Checking for similarity of: date
## In reference but not in current: 0
## In current but not in reference: 4
##
## Checking for similarity of: state
## In reference but not in current:
## In current but not in reference:
##
##
## ***Differences of at least 1 and at least 1%
##
## [1] date name newValue refValue absDelta pctDelta
## <0 rows> (or 0-length row.names)##
##
## ***Differences of at least 0 and at least 0.1%
##
## [1] state name newValue refValue absDelta pctDelta
## <0 rows> (or 0-length row.names)
##
##
##
## Raw file for vax:
## Rows: 36,184
## Columns: 96
## $ date <date> 2022-08-31, 2022-08-31, 2022-0…
## $ MMWR_week <dbl> 35, 35, 35, 35, 35, 35, 35, 35,…
## $ state <chr> "PW", "SD", "MA", "HI", "RI", "…
## $ Distributed <dbl> 47090, 2141765, 18793570, 38391…
## $ Distributed_Janssen <dbl> 3800, 92800, 626200, 124700, 90…
## $ Distributed_Moderna <dbl> 30000, 847500, 7168380, 1461820…
## $ Distributed_Pfizer <dbl> 13290, 1199665, 10993590, 22498…
## $ Distributed_Novavax <dbl> 0, 1800, 5400, 2800, 3200, 200,…
## $ Distributed_Unk_Manuf <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
## $ Dist_Per_100K <dbl> 218698, 242101, 272667, 271153,…
## $ Distributed_Per_100k_5Plus <dbl> 231139, 260083, 287577, 288518,…
## $ Distributed_Per_100k_12Plus <dbl> 252561, 290172, 312354, 316997,…
## $ Distributed_Per_100k_18Plus <dbl> 283966, 320836, 339252, 344011,…
## $ Distributed_Per_100k_65Plus <dbl> 2363960, 1410250, 1607210, 1430…
## $ vxa <dbl> 49416, 1511407, 15773792, 31561…
## $ Administered_5Plus <dbl> 49373, 1507671, 15687231, 31448…
## $ Administered_12Plus <dbl> 46683, 1451767, 15028043, 30184…
## $ Administered_18Plus <dbl> 43018, 1359766, 14038745, 28178…
## $ Administered_65Plus <dbl> 5346, 453691, 3769576, 842523, …
## $ Administered_Janssen <dbl> 2357, 42334, 407539, 71355, 664…
## $ Administered_Moderna <dbl> 37794, 586034, 6193704, 1157104…
## $ Administered_Pfizer <dbl> 9098, 882891, 9171774, 1927032,…
## $ Administered_Novavax <dbl> 0, 0, 295, 10, 219, 1, 45, 25, …
## $ Administered_Unk_Manuf <dbl> 167, 148, 480, 697, 2259, 9, 25…
## $ Admin_Per_100k <dbl> 229500, 170846, 228854, 222915,…
## $ Admin_Per_100k_5Plus <dbl> 242345, 183083, 240044, 236338,…
## $ Admin_Per_100k_12Plus <dbl> 250378, 196689, 249770, 249232,…
## $ Admin_Per_100k_18Plus <dbl> 259410, 203693, 253421, 252497,…
## $ Admin_Per_100k_65Plus <dbl> 268373, 298734, 322370, 313850,…
## $ Recip_Administered <dbl> 49797, 1533994, 15858274, 31873…
## $ Administered_Dose1_Recip <dbl> 20575, 700878, 6982383, 1266721…
## $ Administered_Dose1_Pop_Pct <dbl> 95.0, 79.2, 95.0, 89.5, 95.0, 8…
## $ Administered_Dose1_Recip_5Plus <dbl> 20547, 698199, 6928829, 1259039…
## $ Administered_Dose1_Recip_5PlusPop_Pct <dbl> 95.0, 84.8, 95.0, 94.6, 95.0, 9…
## $ Administered_Dose1_Recip_12Plus <dbl> 19119, 668658, 6593289, 1196906…
## $ Administered_Dose1_Recip_12PlusPop_Pct <dbl> 95.0, 90.6, 95.0, 95.0, 95.0, 9…
## $ Administered_Dose1_Recip_18Plus <dbl> 17584, 622099, 6127404, 1107067…
## $ Administered_Dose1_Recip_18PlusPop_Pct <dbl> 95.0, 93.2, 95.0, 95.0, 95.0, 9…
## $ Administered_Dose1_Recip_65Plus <dbl> 1876, 182192, 1462735, 275645, …
## $ Administered_Dose1_Recip_65PlusPop_Pct <dbl> 94.2, 95.0, 95.0, 95.0, 95.0, 8…
## $ vxc <dbl> 18338, 563276, 5570460, 1128707…
## $ vxcpoppct <dbl> 85.2, 63.7, 80.8, 79.7, 84.9, 8…
## $ Series_Complete_5Plus <dbl> 18330, 563050, 5551263, 1126786…
## $ Series_Complete_5PlusPop_Pct <dbl> 90.0, 68.4, 84.9, 84.7, 89.4, 9…
## $ Series_Complete_12Plus <dbl> 17241, 539812, 5278994, 1071938…
## $ Series_Complete_12PlusPop_Pct <dbl> 92.5, 73.1, 87.7, 88.5, 92.3, 9…
## $ vxcgte18 <dbl> 15791, 503622, 4896487, 990569,…
## $ vxcgte18pct <dbl> 95.0, 75.4, 88.4, 88.8, 93.0, 9…
## $ vxcgte65 <dbl> 1811, 151094, 1165453, 252765, …
## $ vxcgte65pct <dbl> 90.9, 95.0, 95.0, 94.2, 95.0, 8…
## $ Series_Complete_Janssen <dbl> 2361, 39918, 384642, 66056, 611…
## $ Series_Complete_Moderna <dbl> 12724, 204498, 1968460, 371324,…
## $ Series_Complete_Pfizer <dbl> 3164, 318781, 3216940, 691089, …
## $ Series_Complete_Novavax <dbl> 0, 2, 38, 1, 52, 1, 6, 6, 38, 2…
## $ Series_Complete_Unk_Manuf <dbl> 82, 70, 290, 215, 602, 3, 591, …
## $ Series_Complete_Janssen_5Plus <dbl> 2361, 39914, 384637, 66028, 611…
## $ Series_Complete_Moderna_5Plus <dbl> 12724, 204289, 1955713, 370535,…
## $ Series_Complete_Pfizer_5Plus <dbl> 3163, 318775, 3210586, 690007, …
## $ Series_Complete_Unk_Manuf_5Plus <dbl> 82, 70, 289, 215, 587, 3, 591, …
## $ Series_Complete_Janssen_12Plus <dbl> 2361, 39912, 384612, 66026, 611…
## $ Series_Complete_Moderna_12Plus <dbl> 12724, 204257, 1953836, 370413,…
## $ Series_Complete_Pfizer_12Plus <dbl> 2074, 295572, 2940222, 635307, …
## $ Series_Complete_Unk_Manuf_12Plus <dbl> 82, 69, 286, 191, 572, 3, 588, …
## $ Series_Complete_Janssen_18Plus <dbl> 2361, 39882, 383306, 65839, 611…
## $ Series_Complete_Moderna_18Plus <dbl> 12723, 204149, 1948279, 369555,…
## $ Series_Complete_Pfizer_18Plus <dbl> 625, 259525, 2564603, 555015, 4…
## $ Series_Complete_Unk_Manuf_18Plus <dbl> 82, 64, 262, 159, 543, 3, 574, …
## $ Series_Complete_Janssen_65Plus <dbl> 227, 5079, 74665, 11821, 6832, …
## $ Series_Complete_Moderna_65Plus <dbl> 1542, 74263, 531381, 111196, 86…
## $ Series_Complete_Pfizer_65Plus <dbl> 40, 71727, 559321, 129727, 1005…
## $ Series_Complete_Unk_Manuf_65Plus <dbl> 2, 25, 80, 21, 162, 0, 263, 69,…
## $ Additional_Doses <dbl> 12048, 248903, 2987198, 646528,…
## $ Additional_Doses_Vax_Pct <dbl> 65.7, 44.2, 53.6, 57.3, 56.1, 5…
## $ Additional_Doses_5Plus <dbl> 12048, 248900, 2987162, 646515,…
## $ Additional_Doses_5Plus_Vax_Pct <dbl> 65.7, 44.2, 53.8, 57.4, 56.2, 5…
## $ Additional_Doses_12Plus <dbl> 11872, 246180, 2938665, 637193,…
## $ Additional_Doses_12Plus_Vax_Pct <dbl> 68.9, 45.6, 55.7, 59.4, 58.4, 5…
## $ Additional_Doses_18Plus <dbl> 11181, 236970, 2791202, 606621,…
## $ Additional_Doses_18Plus_Vax_Pct <dbl> 70.8, 47.1, 57.0, 61.2, 60.1, 5…
## $ Additional_Doses_50Plus <dbl> 4815, 163923, 1630617, 376685, …
## $ Additional_Doses_50Plus_Vax_Pct <dbl> 80.1, 58.2, 66.0, 75.0, 71.4, 7…
## $ Additional_Doses_65Plus <dbl> 1575, 98849, 840257, 208155, 15…
## $ Additional_Doses_65Plus_Vax_Pct <dbl> 87.0, 65.4, 72.1, 82.4, 79.0, 7…
## $ Additional_Doses_Moderna <dbl> 10870, 109000, 1349812, 272149,…
## $ Additional_Doses_Pfizer <dbl> 1176, 136782, 1609614, 367759, …
## $ Additional_Doses_Janssen <dbl> 2, 3093, 27721, 6500, 5296, 217…
## $ Additional_Doses_Unk_Manuf <dbl> 0, 26, 45, 118, 129, 0, 438, 78…
## $ Second_Booster <dbl> NA, NA, NA, NA, NA, NA, NA, NA,…
## $ Second_Booster_50Plus <dbl> 1126, 53725, 590595, 170399, 10…
## $ Second_Booster_50Plus_Vax_Pct <dbl> 23.4, 32.8, 36.2, 45.2, 34.4, 1…
## $ Second_Booster_65Plus <dbl> 383, 39382, 379347, 111314, 667…
## $ Second_Booster_65Plus_Vax_Pct <dbl> 24.3, 39.8, 45.1, 53.5, 43.5, 2…
## $ Second_Booster_Janssen <dbl> 0, 27, 253, 120, 151, 1, 119, 2…
## $ Second_Booster_Moderna <dbl> 1148, 24919, 309097, 87335, 498…
## $ Second_Booster_Pfizer <dbl> 22, 30731, 314869, 91565, 57827…
## $ Second_Booster_Unk_Manuf <dbl> 0, 2, 10, 15, 53, 0, 80, 28, 21…
##
## Column sums before and after applying filtering rules:
## # A tibble: 3 × 6
## isType tot_cases tot_deaths new_cases new_deaths n
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 before 3.53e+10 5.16e+8 93993694 1025546 56522
## 2 after 3.51e+10 5.14e+8 92929415 1019927 48858
## 3 pctchg 6.80e- 3 4.57e-3 0.0113 0.00548 0.136
##
##
## Processed for cdcDaily:
## Rows: 48,858
## Columns: 6
## $ date <date> 2021-03-11, 2021-12-01, 2021-09-01, 2021-03-08, 2021-09-17…
## $ state <chr> "KS", "ND", "ND", "IN", "FL", "TN", "IA", "SD", "HI", "MA",…
## $ tot_cases <dbl> 297229, 163565, 118491, 668765, 3510205, 64885, 20015, 1226…
## $ tot_deaths <dbl> 4851, 1907, 1562, 12710, 56036, 749, 561, 1967, 17, 17818, …
## $ new_cases <dbl> 0, 589, 536, 487, 9979, 1816, 317, 28, 8, 451, 1040, 133, 0…
## $ new_deaths <dbl> 0, 9, 1, 7, 294, 8, 12, 0, 0, 5, 4, 0, 0, 5, 1, 3, 0, 0, 22…
##
## Column sums before and after applying filtering rules:
## # A tibble: 3 × 5
## isType inp hosp_adult hosp_ped n
## <chr> <dbl> <dbl> <dbl> <dbl>
## 1 before 5.11e+7 4.45e+7 1229807 49367
## 2 after 5.09e+7 4.43e+7 1205197 47181
## 3 pctchg 5.37e-3 5.13e-3 0.0200 0.0443
##
##
## Processed for cdcHosp:
## Rows: 47,181
## Columns: 5
## $ date <date> 2021-01-06, 2021-01-06, 2020-12-31, 2020-12-30, 2020-12-29…
## $ state <chr> "MA", "OR", "SD", "RI", "OR", "OH", "LA", "WV", "VT", "WY",…
## $ inp <dbl> 2232, 583, 282, 471, 626, 5534, 1461, 242, 0, 71, 1, 91, 49…
## $ hosp_adult <dbl> 2209, 568, 280, 469, 615, 5443, 1449, 241, 0, 70, 1, 90, 48…
## $ hosp_ped <dbl> 23, 15, 2, 2, 11, 91, 12, 1, 0, 1, 0, 1, 1, 0, 1, 1, 0, 0, …
##
## Column sums before and after applying filtering rules:
## # A tibble: 3 × 9
## isType vxa vxc vxcpoppct vxcgte65 vxcgt…¹ vxcgte18 vxcgt…² n
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 before 4.14e+11 1.70e+11 1511640. 4.31e+10 2.24e+6 1.57e+11 1.78e+6 3.62e+4
## 2 after 2.00e+11 8.22e+10 1265373. 2.09e+10 1.98e+6 7.61e+10 1.51e+6 2.86e+4
## 3 pctchg 5.18e- 1 5.16e- 1 0.163 5.16e- 1 1.14e-1 5.16e- 1 1.54e-1 2.09e-1
## # … with abbreviated variable names ¹vxcgte65pct, ²vxcgte18pct
##
##
## Processed for vax:
## Rows: 28,611
## Columns: 9
## $ date <date> 2022-08-31, 2022-08-31, 2022-08-31, 2022-08-31, 2022-08-3…
## $ state <chr> "SD", "MA", "HI", "RI", "MT", "WY", "LA", "KS", "IN", "MS"…
## $ vxa <dbl> 1511407, 15773792, 3156198, 2353711, 1675440, 778457, 6536…
## $ vxc <dbl> 563276, 5570460, 1128707, 899544, 618143, 300240, 2526855,…
## $ vxcpoppct <dbl> 63.7, 80.8, 79.7, 84.9, 57.8, 51.9, 54.4, 63.3, 56.8, 53.0…
## $ vxcgte65 <dbl> 151094, 1165453, 252765, 194268, 180238, 84688, 642150, 44…
## $ vxcgte65pct <dbl> 95.0, 95.0, 94.2, 95.0, 87.3, 85.4, 86.7, 94.6, 88.5, 85.2…
## $ vxcgte18 <dbl> 503622, 4896487, 990569, 794734, 562722, 275699, 2323648, …
## $ vxcgte18pct <dbl> 75.4, 88.4, 88.8, 93.0, 67.0, 62.0, 65.2, 74.2, 67.0, 63.3…
##
## Integrated per capita data file:
## Rows: 49,071
## Columns: 34
## $ date <date> 2020-01-01, 2020-01-01, 2020-01-01, 2020-01-01, 2020-01-0…
## $ state <chr> "AL", "HI", "IN", "LA", "MN", "MT", "NC", "TX", "AL", "HI"…
## $ tot_cases <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ tot_deaths <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ new_cases <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ new_deaths <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ inp <dbl> NA, 0, 0, NA, 0, 0, 0, 0, NA, 0, 0, NA, 0, 0, 0, 1877, 0, …
## $ hosp_adult <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ hosp_ped <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ vxa <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ vxc <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ vxcpoppct <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ vxcgte65 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ vxcgte65pct <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ vxcgte18 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ vxcgte18pct <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ tcpm <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ tdpm <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ cpm <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ dpm <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ hpm <dbl> NA, 0.0000, 0.0000, NA, 0.0000, 0.0000, 0.0000, 0.0000, NA…
## $ ahpm <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ phpm <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ vxapm <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ vxcpm <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ tcpm7 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ tdpm7 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ cpm7 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ dpm7 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ hpm7 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ ahpm7 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ phpm7 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ vxapm7 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ vxcpm7 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…## Warning in showSRID(uprojargs, format = "PROJ", multiline = "NO", prefer_proj =
## prefer_proj): Discarded datum unknown in Proj4 definition
saveToRDS(cdc_daily_220907, ovrWriteError=FALSE)The function is run to download and process the latest hospitalization data:
# Run for latest data, save as RDS
indivHosp_20220907 <- downloadReadHospitalData(loc="./RInputFiles/Coronavirus/HHS_Hospital_20220907.csv")##
## File ./RInputFiles/Coronavirus/HHS_Hospital_20220907.csv already exists
## File will not be downloaded since ovrWrite is not TRUE## Rows: 269456 Columns: 128
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (11): hospital_pk, state, ccn, hospital_name, address, city, zip, hosp...
## dbl (114): total_beds_7_day_avg, all_adult_hospital_beds_7_day_avg, all_adu...
## lgl (2): is_metro_micro, is_corrected
## date (1): collection_week
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.## Rows: 269,456
## Columns: 128
## $ hospital_pk <chr> …
## $ collection_week <date> …
## $ state <chr> …
## $ ccn <chr> …
## $ hospital_name <chr> …
## $ address <chr> …
## $ city <chr> …
## $ zip <chr> …
## $ hospital_subtype <chr> …
## $ fips_code <chr> …
## $ is_metro_micro <lgl> …
## $ total_beds_7_day_avg <dbl> …
## $ all_adult_hospital_beds_7_day_avg <dbl> …
## $ all_adult_hospital_inpatient_beds_7_day_avg <dbl> …
## $ inpatient_beds_used_7_day_avg <dbl> …
## $ all_adult_hospital_inpatient_bed_occupied_7_day_avg <dbl> …
## $ inpatient_beds_used_covid_7_day_avg <dbl> …
## $ total_adult_patients_hospitalized_confirmed_and_suspected_covid_7_day_avg <dbl> …
## $ total_adult_patients_hospitalized_confirmed_covid_7_day_avg <dbl> …
## $ total_pediatric_patients_hospitalized_confirmed_and_suspected_covid_7_day_avg <dbl> …
## $ total_pediatric_patients_hospitalized_confirmed_covid_7_day_avg <dbl> …
## $ inpatient_beds_7_day_avg <dbl> …
## $ total_icu_beds_7_day_avg <dbl> …
## $ total_staffed_adult_icu_beds_7_day_avg <dbl> …
## $ icu_beds_used_7_day_avg <dbl> …
## $ staffed_adult_icu_bed_occupancy_7_day_avg <dbl> …
## $ staffed_icu_adult_patients_confirmed_and_suspected_covid_7_day_avg <dbl> …
## $ staffed_icu_adult_patients_confirmed_covid_7_day_avg <dbl> …
## $ total_patients_hospitalized_confirmed_influenza_7_day_avg <dbl> …
## $ icu_patients_confirmed_influenza_7_day_avg <dbl> …
## $ total_patients_hospitalized_confirmed_influenza_and_covid_7_day_avg <dbl> …
## $ total_beds_7_day_sum <dbl> …
## $ all_adult_hospital_beds_7_day_sum <dbl> …
## $ all_adult_hospital_inpatient_beds_7_day_sum <dbl> …
## $ inpatient_beds_used_7_day_sum <dbl> …
## $ all_adult_hospital_inpatient_bed_occupied_7_day_sum <dbl> …
## $ inpatient_beds_used_covid_7_day_sum <dbl> …
## $ total_adult_patients_hospitalized_confirmed_and_suspected_covid_7_day_sum <dbl> …
## $ total_adult_patients_hospitalized_confirmed_covid_7_day_sum <dbl> …
## $ total_pediatric_patients_hospitalized_confirmed_and_suspected_covid_7_day_sum <dbl> …
## $ total_pediatric_patients_hospitalized_confirmed_covid_7_day_sum <dbl> …
## $ inpatient_beds_7_day_sum <dbl> …
## $ total_icu_beds_7_day_sum <dbl> …
## $ total_staffed_adult_icu_beds_7_day_sum <dbl> …
## $ icu_beds_used_7_day_sum <dbl> …
## $ staffed_adult_icu_bed_occupancy_7_day_sum <dbl> …
## $ staffed_icu_adult_patients_confirmed_and_suspected_covid_7_day_sum <dbl> …
## $ staffed_icu_adult_patients_confirmed_covid_7_day_sum <dbl> …
## $ total_patients_hospitalized_confirmed_influenza_7_day_sum <dbl> …
## $ icu_patients_confirmed_influenza_7_day_sum <dbl> …
## $ total_patients_hospitalized_confirmed_influenza_and_covid_7_day_sum <dbl> …
## $ total_beds_7_day_coverage <dbl> …
## $ all_adult_hospital_beds_7_day_coverage <dbl> …
## $ all_adult_hospital_inpatient_beds_7_day_coverage <dbl> …
## $ inpatient_beds_used_7_day_coverage <dbl> …
## $ all_adult_hospital_inpatient_bed_occupied_7_day_coverage <dbl> …
## $ inpatient_beds_used_covid_7_day_coverage <dbl> …
## $ total_adult_patients_hospitalized_confirmed_and_suspected_covid_7_day_coverage <dbl> …
## $ total_adult_patients_hospitalized_confirmed_covid_7_day_coverage <dbl> …
## $ total_pediatric_patients_hospitalized_confirmed_and_suspected_covid_7_day_coverage <dbl> …
## $ total_pediatric_patients_hospitalized_confirmed_covid_7_day_coverage <dbl> …
## $ inpatient_beds_7_day_coverage <dbl> …
## $ total_icu_beds_7_day_coverage <dbl> …
## $ total_staffed_adult_icu_beds_7_day_coverage <dbl> …
## $ icu_beds_used_7_day_coverage <dbl> …
## $ staffed_adult_icu_bed_occupancy_7_day_coverage <dbl> …
## $ staffed_icu_adult_patients_confirmed_and_suspected_covid_7_day_coverage <dbl> …
## $ staffed_icu_adult_patients_confirmed_covid_7_day_coverage <dbl> …
## $ total_patients_hospitalized_confirmed_influenza_7_day_coverage <dbl> …
## $ icu_patients_confirmed_influenza_7_day_coverage <dbl> …
## $ total_patients_hospitalized_confirmed_influenza_and_covid_7_day_coverage <dbl> …
## $ previous_day_admission_adult_covid_confirmed_7_day_sum <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_18-19_7_day_sum` <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_20-29_7_day_sum` <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_30-39_7_day_sum` <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_40-49_7_day_sum` <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_50-59_7_day_sum` <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_60-69_7_day_sum` <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_70-79_7_day_sum` <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_80+_7_day_sum` <dbl> …
## $ previous_day_admission_adult_covid_confirmed_unknown_7_day_sum <dbl> …
## $ previous_day_admission_pediatric_covid_confirmed_7_day_sum <dbl> …
## $ previous_day_covid_ED_visits_7_day_sum <dbl> …
## $ previous_day_admission_adult_covid_suspected_7_day_sum <dbl> …
## $ `previous_day_admission_adult_covid_suspected_18-19_7_day_sum` <dbl> …
## $ `previous_day_admission_adult_covid_suspected_20-29_7_day_sum` <dbl> …
## $ `previous_day_admission_adult_covid_suspected_30-39_7_day_sum` <dbl> …
## $ `previous_day_admission_adult_covid_suspected_40-49_7_day_sum` <dbl> …
## $ `previous_day_admission_adult_covid_suspected_50-59_7_day_sum` <dbl> …
## $ `previous_day_admission_adult_covid_suspected_60-69_7_day_sum` <dbl> …
## $ `previous_day_admission_adult_covid_suspected_70-79_7_day_sum` <dbl> …
## $ `previous_day_admission_adult_covid_suspected_80+_7_day_sum` <dbl> …
## $ previous_day_admission_adult_covid_suspected_unknown_7_day_sum <dbl> …
## $ previous_day_admission_pediatric_covid_suspected_7_day_sum <dbl> …
## $ previous_day_total_ED_visits_7_day_sum <dbl> …
## $ previous_day_admission_influenza_confirmed_7_day_sum <dbl> …
## $ geocoded_hospital_address <chr> …
## $ hhs_ids <chr> …
## $ previous_day_admission_adult_covid_confirmed_7_day_coverage <dbl> …
## $ previous_day_admission_pediatric_covid_confirmed_7_day_coverage <dbl> …
## $ previous_day_admission_adult_covid_suspected_7_day_coverage <dbl> …
## $ previous_day_admission_pediatric_covid_suspected_7_day_coverage <dbl> …
## $ previous_week_personnel_covid_vaccinated_doses_administered_7_day <dbl> …
## $ total_personnel_covid_vaccinated_doses_none_7_day <dbl> …
## $ total_personnel_covid_vaccinated_doses_one_7_day <dbl> …
## $ total_personnel_covid_vaccinated_doses_all_7_day <dbl> …
## $ previous_week_patients_covid_vaccinated_doses_one_7_day <dbl> …
## $ previous_week_patients_covid_vaccinated_doses_all_7_day <dbl> …
## $ is_corrected <lgl> …
## $ all_pediatric_inpatient_bed_occupied_7_day_avg <dbl> …
## $ all_pediatric_inpatient_bed_occupied_7_day_coverage <dbl> …
## $ all_pediatric_inpatient_bed_occupied_7_day_sum <dbl> …
## $ all_pediatric_inpatient_beds_7_day_avg <dbl> …
## $ all_pediatric_inpatient_beds_7_day_coverage <dbl> …
## $ all_pediatric_inpatient_beds_7_day_sum <dbl> …
## $ previous_day_admission_pediatric_covid_confirmed_0_4_7_day_sum <dbl> …
## $ previous_day_admission_pediatric_covid_confirmed_12_17_7_day_sum <dbl> …
## $ previous_day_admission_pediatric_covid_confirmed_5_11_7_day_sum <dbl> …
## $ previous_day_admission_pediatric_covid_confirmed_unknown_7_day_sum <dbl> …
## $ staffed_icu_pediatric_patients_confirmed_covid_7_day_avg <dbl> …
## $ staffed_icu_pediatric_patients_confirmed_covid_7_day_coverage <dbl> …
## $ staffed_icu_pediatric_patients_confirmed_covid_7_day_sum <dbl> …
## $ staffed_pediatric_icu_bed_occupancy_7_day_avg <dbl> …
## $ staffed_pediatric_icu_bed_occupancy_7_day_coverage <dbl> …
## $ staffed_pediatric_icu_bed_occupancy_7_day_sum <dbl> …
## $ total_staffed_pediatric_icu_beds_7_day_avg <dbl> …
## $ total_staffed_pediatric_icu_beds_7_day_coverage <dbl> …
## $ total_staffed_pediatric_icu_beds_7_day_sum <dbl> …
##
## Hospital Subtype Counts:
## # A tibble: 4 × 2
## hospital_subtype n
## <chr> <int>
## 1 Childrens Hospitals 5077
## 2 Critical Access Hospitals 72331
## 3 Long Term 18519
## 4 Short Term 173529
##
## Records other than 50 states and DC
## # A tibble: 5 × 2
## state n
## <chr> <int>
## 1 AS 54
## 2 GU 106
## 3 MP 46
## 4 PR 2864
## 5 VI 106
##
## Record types for key metrics
## # A tibble: 10 × 5
## name `NA` Posit…¹ Value…² Total
## <chr> <int> <int> <int> <int>
## 1 all_adult_hospital_beds_7_day_avg 64869 204079 508 269456
## 2 all_adult_hospital_inpatient_bed_occupied_7_day… 143 247227 22086 269456
## 3 icu_beds_used_7_day_avg 64 237310 32082 269456
## 4 inpatient_beds_7_day_avg 67 268366 1023 269456
## 5 inpatient_beds_used_7_day_avg 51 248042 21363 269456
## 6 inpatient_beds_used_covid_7_day_avg 32 182000 87424 269456
## 7 staffed_icu_adult_patients_confirmed_and_suspec… 162 184312 84982 269456
## 8 total_adult_patients_hospitalized_confirmed_and… 121 181944 87391 269456
## 9 total_beds_7_day_avg 63149 206009 298 269456
## 10 total_icu_beds_7_day_avg 74 255402 13980 269456
## # … with abbreviated variable names ¹Positive, ²`Value -999999`## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
saveToRDS(indivHosp_20220907, ovrWriteError=FALSE)##
## File already exists: ./RInputFiles/Coronavirus/indivHosp_20220907.RDS
##
## Not replacing the existing file since ovrWrite=FALSE## NULLPost-processing is run, including hospital summaries:
# Create pivoted burden data
burdenPivotList_220907 <- postProcessCDCDaily(cdc_daily_220907,
dataThruLabel="Aug 2022",
keyDatesBurden=c("2022-08-31", "2022-02-28",
"2021-08-31", "2021-02-28"
),
keyDatesVaccine=c("2022-08-31", "2022-03-31",
"2021-10-31", "2021-05-31"
),
returnData=TRUE
)## Joining, by = "state"##
## *** File has been checked for uniqueness by: state date name## Warning: Removed 24 row(s) containing missing values (geom_path).
## Warning: Removed 24 rows containing missing values (position_stack).
## Warning: Removed 24 rows containing missing values (position_stack).
## Warning: Removed 9 row(s) containing missing values (geom_path).
# Create hospitalized per capita data
hospPerCap_220907 <- hospAgePerCapita(readFromRDS("dfStateAgeBucket2019"),
lst=burdenPivotList_220907,
popVar="pop2019",
excludeState=c(),
cumStartDate="2020-07-15"
)## Warning: Removed 18 row(s) containing missing values (geom_path).
burdenPivotList_220907$hospAge %>%
group_by(adultPed, confSusp, age, name) %>%
summarize(value=sum(value, na.rm=TRUE), n=n(), .groups="drop")## # A tibble: 18 × 6
## adultPed confSusp age name value n
## <chr> <chr> <chr> <chr> <dbl> <int>
## 1 adult confirmed 0-19 previous_day_admission_adult_covid_con… 4.70e4 49367
## 2 adult confirmed 20-29 previous_day_admission_adult_covid_con… 2.86e5 49367
## 3 adult confirmed 30-39 previous_day_admission_adult_covid_con… 4.12e5 49367
## 4 adult confirmed 40-49 previous_day_admission_adult_covid_con… 4.96e5 49367
## 5 adult confirmed 50-59 previous_day_admission_adult_covid_con… 7.91e5 49367
## 6 adult confirmed 60-69 previous_day_admission_adult_covid_con… 1.04e6 49367
## 7 adult confirmed 70-79 previous_day_admission_adult_covid_con… 1.04e6 49367
## 8 adult confirmed 80+ previous_day_admission_adult_covid_con… 9.32e5 49367
## 9 adult suspected 0-19 previous_day_admission_adult_covid_sus… 3.83e4 49367
## 10 adult suspected 20-29 previous_day_admission_adult_covid_sus… 2.56e5 49367
## 11 adult suspected 30-39 previous_day_admission_adult_covid_sus… 3.35e5 49367
## 12 adult suspected 40-49 previous_day_admission_adult_covid_sus… 3.39e5 49367
## 13 adult suspected 50-59 previous_day_admission_adult_covid_sus… 5.37e5 49367
## 14 adult suspected 60-69 previous_day_admission_adult_covid_sus… 7.38e5 49367
## 15 adult suspected 70-79 previous_day_admission_adult_covid_sus… 7.19e5 49367
## 16 adult suspected 80+ previous_day_admission_adult_covid_sus… 6.55e5 49367
## 17 ped confirmed 0-19 previous_day_admission_pediatric_covid… 1.67e5 49367
## 18 ped suspected 0-19 previous_day_admission_pediatric_covid… 3.74e5 49367saveToRDS(burdenPivotList_220907, ovrWriteError=FALSE)
saveToRDS(hospPerCap_220907, ovrWriteError=FALSE)Peaks and valleys of key metrics are also updated:
peakValleyCDCDaily(cdc_daily_220907)## Warning: Removed 6 row(s) containing missing values (geom_path).
## Warning: Removed 6 row(s) containing missing values (geom_path).
## Warning: Removed 6 row(s) containing missing values (geom_path).
## Warning: Removed 20 row(s) containing missing values (geom_path).
## Warning: Removed 20 row(s) containing missing values (geom_path).
## # A tibble: 7,740 × 8
## date state vxa vxc vxa_isPeak vxc_isPeak vxa_isValley vxc_isValley
## <date> <chr> <dbl> <dbl> <lgl> <lgl> <lgl> <lgl>
## 1 2020-12-01 CA NA NA FALSE FALSE FALSE FALSE
## 2 2020-12-01 FL NA NA FALSE FALSE FALSE FALSE
## 3 2020-12-01 GA NA NA FALSE FALSE FALSE FALSE
## 4 2020-12-01 IL NA NA FALSE FALSE FALSE FALSE
## 5 2020-12-01 MI NA NA FALSE FALSE FALSE FALSE
## 6 2020-12-01 NC NA NA FALSE FALSE FALSE FALSE
## 7 2020-12-01 NJ NA NA FALSE FALSE FALSE FALSE
## 8 2020-12-01 NY NA NA FALSE FALSE FALSE FALSE
## 9 2020-12-01 OH NA NA FALSE FALSE FALSE FALSE
## 10 2020-12-01 PA NA NA FALSE FALSE FALSE FALSE
## # … with 7,730 more rows
## # ℹ Use `print(n = ...)` to see more rowsHospital capacity is updated using a mix of old data (for 2021) and new data:
identical(names(indivHosp_20220907), names(readFromRDS("indivHosp_20220704")))## [1] TRUEmodHospData <- bind_rows(filter(readFromRDS("indivHosp_20220704"), lubridate::year(collection_week)<2022),
filter(indivHosp_20220907, lubridate::year(collection_week)>=2022),
.id="src"
)
updated_modStateHosp_20220907 <- hospitalCapacityCDCDaily(modHospData,
plotSub="Aug 2020 to Aug 2022\nOld data used pre-2022"
)
Data availability by source and time is assessed:
# Temporary function to aggregate data
tempCounter <- function(df) {
df %>%
select(hospital_pk, collection_week, all_of(names(hhsMapper))) %>%
colRenamer(vecRename=hhsMapper) %>%
pivot_longer(-c(hospital_pk, collection_week)) %>%
filter(!is.na(value), value>0) %>%
count(collection_week, name)
}
dfTemp <- bind_rows(tempCounter(indivHosp_20220907), tempCounter(readFromRDS("indivHosp_20220704")), .id="src")
dfTemp %>%
select(collection_week, name) %>%
unique() %>%
bind_rows(., ., .id="src") %>%
full_join(dfTemp, by=c("src", "collection_week", "name")) %>%
mutate(src=c("1"="SEP-2022", "2"="JUL-2022")[src]) %>%
mutate(n=ifelse(is.na(n), 0, n)) %>%
ggplot(aes(x=collection_week, y=n)) +
geom_line(aes(group=src, color=src)) +
facet_wrap(~name) +
labs(title="Number of hospitals in US reporting >0 on metric by week", x=NULL, y="# Hospitals Reporting > 0") +
scale_color_discrete("Data Source:")
dfTemp %>%
select(collection_week, name) %>%
unique() %>%
bind_rows(., ., .id="src") %>%
full_join(dfTemp, by=c("src", "collection_week", "name")) %>%
mutate(src=c("1"="SEP-2022", "2"="JUL-2022")[src]) %>%
mutate(n=ifelse(is.na(n), 0, n)) %>%
group_by(collection_week, name) %>%
summarize(delta=sum(ifelse(src=="SEP-2022", n, 0)-ifelse(src!="SEP-2022", n, 0)), .groups="drop") %>%
ggplot(aes(x=collection_week, y=delta)) +
geom_line(aes(color=case_when(delta>=0 ~ "darkgreen", TRUE ~ "red"))) +
geom_hline(yintercept=0, lty=2) +
geom_vline(xintercept=c(as.Date("2021-08-20"), as.Date("2022-06-24")), lty=2) +
scale_color_identity(NULL) +
facet_wrap(~name) +
labs(title="Delta in Number of hospitals in US reporting >0 on metric by week",
subtitle="Trend break dashed lines at 2021-08-20 and 2022-06-24",
x=NULL,
y="Delta in # Hospitals Reporting > 0"
)
The process is converted to functional form:
multiSourceDataCombine <- function(lst, timeVec, keyVar="collection_week", idName="src") {
# FUNCTION ARGUMENTS:
# lst: list of data frames to be combined
# timeVec: vector of time cut points (data before timeVec[1] taken from lst[[1]], etc.)
# keyVar: variable describing time in the data
# idName: name of column for .id when files combined
# Check list lengths
if(length(lst)==0) {
cat("\nEmpty list passed, returning 0x0 tibble\n")
return(tibble::tibble())
} else if (length(lst)==1) {
cat("\nList of length 1 passed, returning item in list as-is")
return(lst[[1]])
}
# Check that timeVec matches
if(length(lst) != length(timeVec) + 1) stop("\nMismatch of lst and timeVec\n")
# Check that all data frames have the same column names in the same order
vecNames <- names(lst[[1]])
for(n in 2:length(lst)) if(!isTRUE(identical(names(lst[[n]]), vecNames))) stop("\nName mismatch in files\n")
# Combine data
bind_rows(lst, .id=idName) %>%
mutate(srcNum=as.integer(get(idName)),
dateMin=ifelse(srcNum==1, NA, timeVec[srcNum-1]),
dateMax=ifelse(srcNum==max(srcNum), NA, timeVec[srcNum])
) %>%
filter(is.na(dateMin) | get(keyVar) >= dateMin,
is.na(dateMax) | get(keyVar) < dateMax
) %>%
select(-srcNum, -dateMin, -dateMax)
}
# Create modified hospital data
multiSourceHosp_20220902 <- multiSourceDataCombine(list(readFromRDS("indivHosp_20220704"),
indivHosp_20220907
),
timeVec=as.Date("2022-01-01")
)
# Confirm that function produces expected output
multiSourceHosp_20220902 %>%
select(-src) %>%
identical(modHospData %>% select(-src))## [1] TRUEThe updated hospital data are then plotted:
# Run hospital plots
modStateHosp_20220902 <- hospitalCapacityCDCDaily(multiSourceHosp_20220902,
plotSub="Aug 2020 to Aug 2022\nOld data used pre-2022"
)
Data Refreshes
Data From 2022-10-02
The latest CDC case, hospitalization, and death data are downloaded and processed:
readList <- list("cdcDaily"="./RInputFiles/Coronavirus/CDC_dc_downloaded_221002.csv",
"cdcHosp"="./RInputFiles/Coronavirus/CDC_h_downloaded_221002.csv",
"vax"="./RInputFiles/Coronavirus/vaxData_downloaded_221002.csv"
)
compareList <- list("cdcDaily"=readFromRDS("cdc_daily_220907")$dfRaw$cdcDaily,
"cdcHosp"=readFromRDS("cdc_daily_220907")$dfRaw$cdcHosp,
"vax"=readFromRDS("cdc_daily_220907")$dfRaw$vax
)
cdc_daily_221002 <- readRunCDCDaily(thruLabel="Sep 30, 2022",
downloadTo=lapply(readList, FUN=function(x) if(file.exists(x)) NA else x),
readFrom=readList,
compareFile=compareList,
writeLog=NULL,
useClusters=readFromRDS("cdc_daily_210528")$useClusters,
weightedMeanAggs=c("tcpm7", "tdpm7", "cpm7", "dpm7", "hpm7",
"vxcpm7", "vxcgte65pct"
),
skipAssessmentPlots=FALSE,
brewPalette="Paired"
)## Rows: 58980 Columns: 15
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (5): submission_date, state, created_at, consent_cases, consent_deaths
## dbl (10): tot_cases, conf_cases, prob_cases, new_case, pnew_case, tot_death,...
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.##
## *** File has been checked for uniqueness by: state date
##
##
## Checking for similarity of: column names
## In reference but not in current:
## In current but not in reference:
##
## Checking for similarity of: date
## In reference but not in current: 0
## In current but not in reference: 25
##
## Checking for similarity of: state
## In reference but not in current:
## In current but not in reference:
##
##
## ***Differences of at least 5 and at least 5%
##
## date name newValue refValue absDelta pctDelta
## 1 2022-09-04 new_deaths 109 20 89 1.37984496
## 2 2022-09-03 new_deaths 116 24 92 1.31428571
## 3 2022-08-27 new_deaths 163 91 72 0.56692913
## 4 2022-09-05 new_deaths 106 60 46 0.55421687
## 5 2022-08-28 new_deaths 117 87 30 0.29411765
## 6 2022-08-20 new_deaths 139 111 28 0.22400000
## 7 2021-03-03 new_deaths 1899 1547 352 0.20429483
## 8 2022-08-06 new_deaths 174 142 32 0.20253165
## 9 2022-08-13 new_deaths 137 112 25 0.20080321
## 10 2020-09-14 new_deaths 376 453 77 0.18576598
## 11 2021-03-02 new_deaths 1134 1357 223 0.17904456
## 12 2022-08-21 new_deaths 122 102 20 0.17857143
## 13 2022-07-02 new_deaths 146 124 22 0.16296296
## 14 2022-09-01 new_deaths 499 428 71 0.15318231
## 15 2022-07-30 new_deaths 151 130 21 0.14946619
## 16 2022-07-04 new_deaths 120 138 18 0.13953488
## 17 2021-03-18 new_deaths 961 843 118 0.13082040
## 18 2022-06-27 new_deaths 259 295 36 0.12996390
## 19 2022-09-02 new_deaths 509 453 56 0.11642412
## 20 2022-08-14 new_deaths 115 103 12 0.11009174
## 21 2022-05-30 new_deaths 78 86 8 0.09756098
## 22 2022-04-25 new_deaths 181 199 18 0.09473684
## 23 2021-03-19 new_deaths 1080 1182 102 0.09018568
## 24 2022-07-09 new_deaths 120 110 10 0.08695652
## 25 2022-08-25 new_deaths 536 492 44 0.08560311
## 26 2022-08-30 new_deaths 633 582 51 0.08395062
## 27 2022-04-04 new_deaths 325 353 28 0.08259587
## 28 2022-08-26 new_deaths 674 621 53 0.08185328
## 29 2022-07-16 new_deaths 163 151 12 0.07643312
## 30 2022-08-01 new_deaths 402 433 31 0.07425150
## 31 2021-10-11 new_deaths 983 915 68 0.07165437
## 32 2022-06-20 new_deaths 140 150 10 0.06896552
## 33 2021-12-28 new_deaths 2334 2185 149 0.06594379
## 34 2022-08-31 new_deaths 778 830 52 0.06467662
## 35 2020-09-15 new_deaths 827 777 50 0.06234414
## 36 2020-09-21 new_deaths 547 580 33 0.05856256
## 37 2021-02-28 new_deaths 1027 1088 61 0.05768322
## 38 2022-08-19 new_deaths 622 588 34 0.05619835
## 39 2021-03-01 new_deaths 1333 1262 71 0.05472062
## 40 2021-06-28 new_deaths 183 193 10 0.05319149
## 41 2021-10-04 new_deaths 1298 1234 64 0.05055292
## 42 2022-09-03 new_cases 26751 14942 11809 0.56647399
## 43 2022-09-04 new_cases 26642 18143 8499 0.37954672
## 44 2022-08-27 new_cases 31520 27723 3797 0.12818392
## 45 2022-08-20 new_cases 31149 27757 3392 0.11516654
## 46 2022-07-04 new_cases 46367 50779 4412 0.09083236
## 47 2022-08-06 new_cases 36583 33586 2997 0.08542234
## 48 2022-08-28 new_cases 29299 26941 2358 0.08385491
## 49 2022-08-13 new_cases 33397 30736 2661 0.08298380
## 50 2022-07-30 new_cases 41625 38338 3287 0.08221302
## 51 2022-08-29 new_cases 80569 87383 6814 0.08114223
## 52 2022-08-14 new_cases 25021 23110 1911 0.07940828
## 53 2022-08-21 new_cases 30913 28587 2326 0.07818487
## 54 2022-07-11 new_cases 115394 124421 9027 0.07528303
## 55 2022-09-01 new_cases 106616 99807 6809 0.06597133
## 56 2021-10-04 new_cases 92025 86161 5864 0.06581886
## 57 2021-09-20 new_cases 98599 92534 6065 0.06346366
## 58 2022-07-09 new_cases 58075 54584 3491 0.06197463
## 59 2022-07-23 new_cases 56226 52972 3254 0.05959816
## 60 2022-09-02 new_cases 107474 101329 6145 0.05885931
## 61 2022-07-02 new_cases 51723 48818 2905 0.05778737
## 62 2022-08-07 new_cases 37208 35135 2073 0.05731031
## 63 2021-09-06 new_cases 115878 109526 6352 0.05636102
## 64 2022-07-18 new_cases 103308 109131 5823 0.05482044
## 65 2021-09-13 new_cases 119108 112822 6286 0.05420601
## 66 2022-07-31 new_cases 42016 39803 2213 0.05409501
## 67 2021-09-27 new_cases 107419 101840 5579 0.05332148
## 68 2022-07-16 new_cases 58177 55197 2980 0.05256937
##
##
## ***Differences of at least 0 and at least 0.1%
##
## state name newValue refValue absDelta pctDelta
## 1 VA tot_deaths 9582222 9605584 23362 0.002435088
## 2 KY tot_deaths 7519333 7506070 13263 0.001765410
## 3 VA tot_cases 726116885 728276276 2159391 0.002969474
## 4 KY new_deaths 16974 16757 217 0.012866503
## 5 AL new_deaths 20360 20203 157 0.007741045
## 6 VA new_deaths 21479 21334 145 0.006773644
## 7 FL new_deaths 80737 80209 528 0.006561207
## 8 KS new_deaths 9001 9054 53 0.005870950
## 9 NC new_deaths 26416 26338 78 0.002957122
## 10 OK new_deaths 13708 13746 38 0.002768267
## 11 SC new_deaths 18313 18263 50 0.002734033
## 12 SC new_cases 1688432 1674281 14151 0.008416419
## 13 KY new_cases 1559169 1546719 12450 0.008017031
## 14 NC new_cases 3143278 3123308 19970 0.006373486
## 15 CO new_cases 1637839 1634396 3443 0.002104372
## 16 WA new_cases 1789708 1787212 2496 0.001395614
##
##
##
## Raw file for cdcDaily:
## Rows: 58,980
## Columns: 15
## $ date <date> 2021-03-11, 2021-12-01, 2020-04-07, 2020-04-08, 2020-0…
## $ state <chr> "KS", "ND", "AS", "AR", "AR", "ND", "IN", "AR", "NY", "…
## $ tot_cases <dbl> 297229, 163565, 0, 1071, 0, 118491, 668765, 56199, 1882…
## $ conf_cases <dbl> 241035, 135705, NA, NA, NA, 107475, NA, NA, NA, 144788,…
## $ prob_cases <dbl> 56194, 27860, NA, NA, NA, 11016, NA, NA, NA, 29179, 125…
## $ new_cases <dbl> 0, 589, 0, 78, 0, 536, 487, 547, 318, 667, 154, 1509, 0…
## $ pnew_case <dbl> 0, 220, NA, NA, NA, 66, 0, 0, 0, 274, 43, 0, 0, 616, 0,…
## $ tot_deaths <dbl> 4851, 1907, 0, 18, 0, 1562, 12710, 674, 8822, 2911, 115…
## $ conf_death <dbl> NA, NA, NA, NA, NA, NA, 12315, NA, NA, 2482, 9205, NA, …
## $ prob_death <dbl> NA, NA, NA, NA, NA, NA, 395, NA, NA, 429, 2325, NA, 152…
## $ new_deaths <dbl> 0, 9, 0, 0, 0, 1, 7, 11, 2, 8, 5, 6, 0, 100, 0, 5, 0, 0…
## $ pnew_death <dbl> 0, 0, NA, NA, NA, 0, 2, 0, 0, 3, 1, 0, 0, 34, 0, 0, 0, …
## $ created_at <chr> "03/12/2021 03:20:13 PM", "12/02/2021 02:35:20 PM", "04…
## $ consent_cases <chr> "Agree", "Agree", NA, "Not agree", "Not agree", "Agree"…
## $ consent_deaths <chr> "N/A", "Not agree", NA, "Not agree", "Not agree", "Not …## Rows: 50717 Columns: 135
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (1): state
## dbl (132): critical_staffing_shortage_today_yes, critical_staffing_shortage...
## lgl (1): geocoded_state
## date (1): date
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
##
## *** File has been checked for uniqueness by: state date
##
##
## Checking for similarity of: column names
## In reference but not in current:
## In current but not in reference:
##
## Checking for similarity of: date
## In reference but not in current: 0
## In current but not in reference: 25
##
## Checking for similarity of: state
## In reference but not in current:
## In current but not in reference:
##
##
## ***Differences of at least 5 and at least 5%
##
## date name newValue refValue absDelta pctDelta
## 1 2022-09-06 inp 35899 31213 4686 0.1396472
## 2 2020-07-25 hosp_ped 4543 3964 579 0.1361232
## 3 2022-09-06 hosp_ped 1651 1543 108 0.0676268
## 4 2022-09-06 hosp_adult 34457 29670 4787 0.1492975
##
##
## ***Differences of at least 0 and at least 0.1%
##
## state name newValue refValue absDelta pctDelta
## 1 KY inp 816280 813897 2383 0.002923609
## 2 NC inp 1500803 1499030 1773 0.001182066
## 3 ME hosp_ped 2525 2573 48 0.018830914
## 4 SC hosp_ped 9677 9528 149 0.015516793
## 5 WV hosp_ped 6031 6124 93 0.015302345
## 6 DE hosp_ped 5656 5585 71 0.012632328
## 7 KY hosp_ped 21854 21680 174 0.007993752
## 8 MS hosp_ped 12564 12470 94 0.007509787
## 9 NJ hosp_ped 20426 20300 126 0.006187693
## 10 MA hosp_ped 13350 13270 80 0.006010518
## 11 MD hosp_ped 18441 18334 107 0.005819171
## 12 UT hosp_ped 10918 10980 62 0.005662618
## 13 VA hosp_ped 19166 19260 94 0.004892521
## 14 NM hosp_ped 8390 8351 39 0.004659220
## 15 ID hosp_ped 4294 4275 19 0.004434590
## 16 CO hosp_ped 22949 23039 90 0.003914065
## 17 KS hosp_ped 5109 5128 19 0.003712025
## 18 NV hosp_ped 5680 5661 19 0.003350675
## 19 AR hosp_ped 13462 13506 44 0.003263127
## 20 TN hosp_ped 23438 23376 62 0.002648780
## 21 PA hosp_ped 57278 57151 127 0.002219717
## 22 RI hosp_ped 3719 3727 8 0.002148805
## 23 MT hosp_ped 3511 3518 7 0.001991748
## 24 IL hosp_ped 46107 46022 85 0.001845239
## 25 PR hosp_ped 24430 24470 40 0.001635992
## 26 MO hosp_ped 41624 41556 68 0.001635008
## 27 SD hosp_ped 4436 4443 7 0.001576754
## 28 NC hosp_ped 31811 31761 50 0.001573020
## 29 AL hosp_ped 21961 21929 32 0.001458191
## 30 GA hosp_ped 55159 55238 79 0.001431198
## 31 AK hosp_ped 2922 2926 4 0.001367989
## 32 KY hosp_adult 743511 741160 2351 0.003167032
## 33 NC hosp_adult 1376148 1374437 1711 0.001244099
## 34 VT hosp_adult 25440 25466 26 0.001021491
##
##
##
## Raw file for cdcHosp:
## Rows: 50,717
## Columns: 135
## $ state <chr> …
## $ date <date> …
## $ critical_staffing_shortage_today_yes <dbl> …
## $ critical_staffing_shortage_today_no <dbl> …
## $ critical_staffing_shortage_today_not_reported <dbl> …
## $ critical_staffing_shortage_anticipated_within_week_yes <dbl> …
## $ critical_staffing_shortage_anticipated_within_week_no <dbl> …
## $ critical_staffing_shortage_anticipated_within_week_not_reported <dbl> …
## $ hospital_onset_covid <dbl> …
## $ hospital_onset_covid_coverage <dbl> …
## $ inpatient_beds <dbl> …
## $ inpatient_beds_coverage <dbl> …
## $ inpatient_beds_used <dbl> …
## $ inpatient_beds_used_coverage <dbl> …
## $ inp <dbl> …
## $ inpatient_beds_used_covid_coverage <dbl> …
## $ previous_day_admission_adult_covid_confirmed <dbl> …
## $ previous_day_admission_adult_covid_confirmed_coverage <dbl> …
## $ previous_day_admission_adult_covid_suspected <dbl> …
## $ previous_day_admission_adult_covid_suspected_coverage <dbl> …
## $ previous_day_admission_pediatric_covid_confirmed <dbl> …
## $ previous_day_admission_pediatric_covid_confirmed_coverage <dbl> …
## $ previous_day_admission_pediatric_covid_suspected <dbl> …
## $ previous_day_admission_pediatric_covid_suspected_coverage <dbl> …
## $ staffed_adult_icu_bed_occupancy <dbl> …
## $ staffed_adult_icu_bed_occupancy_coverage <dbl> …
## $ staffed_icu_adult_patients_confirmed_and_suspected_covid <dbl> …
## $ staffed_icu_adult_patients_confirmed_and_suspected_covid_coverage <dbl> …
## $ staffed_icu_adult_patients_confirmed_covid <dbl> …
## $ staffed_icu_adult_patients_confirmed_covid_coverage <dbl> …
## $ hosp_adult <dbl> …
## $ total_adult_patients_hospitalized_confirmed_and_suspected_covid_coverage <dbl> …
## $ total_adult_patients_hospitalized_confirmed_covid <dbl> …
## $ total_adult_patients_hospitalized_confirmed_covid_coverage <dbl> …
## $ hosp_ped <dbl> …
## $ total_pediatric_patients_hospitalized_confirmed_and_suspected_covid_coverage <dbl> …
## $ total_pediatric_patients_hospitalized_confirmed_covid <dbl> …
## $ total_pediatric_patients_hospitalized_confirmed_covid_coverage <dbl> …
## $ total_staffed_adult_icu_beds <dbl> …
## $ total_staffed_adult_icu_beds_coverage <dbl> …
## $ inpatient_beds_utilization <dbl> …
## $ inpatient_beds_utilization_coverage <dbl> …
## $ inpatient_beds_utilization_numerator <dbl> …
## $ inpatient_beds_utilization_denominator <dbl> …
## $ percent_of_inpatients_with_covid <dbl> …
## $ percent_of_inpatients_with_covid_coverage <dbl> …
## $ percent_of_inpatients_with_covid_numerator <dbl> …
## $ percent_of_inpatients_with_covid_denominator <dbl> …
## $ inpatient_bed_covid_utilization <dbl> …
## $ inpatient_bed_covid_utilization_coverage <dbl> …
## $ inpatient_bed_covid_utilization_numerator <dbl> …
## $ inpatient_bed_covid_utilization_denominator <dbl> …
## $ adult_icu_bed_covid_utilization <dbl> …
## $ adult_icu_bed_covid_utilization_coverage <dbl> …
## $ adult_icu_bed_covid_utilization_numerator <dbl> …
## $ adult_icu_bed_covid_utilization_denominator <dbl> …
## $ adult_icu_bed_utilization <dbl> …
## $ adult_icu_bed_utilization_coverage <dbl> …
## $ adult_icu_bed_utilization_numerator <dbl> …
## $ adult_icu_bed_utilization_denominator <dbl> …
## $ geocoded_state <lgl> …
## $ `previous_day_admission_adult_covid_confirmed_18-19` <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_18-19_coverage` <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_20-29` <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_20-29_coverage` <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_30-39` <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_30-39_coverage` <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_40-49` <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_40-49_coverage` <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_50-59` <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_50-59_coverage` <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_60-69` <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_60-69_coverage` <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_70-79` <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_70-79_coverage` <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_80+` <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_80+_coverage` <dbl> …
## $ previous_day_admission_adult_covid_confirmed_unknown <dbl> …
## $ previous_day_admission_adult_covid_confirmed_unknown_coverage <dbl> …
## $ `previous_day_admission_adult_covid_suspected_18-19` <dbl> …
## $ `previous_day_admission_adult_covid_suspected_18-19_coverage` <dbl> …
## $ `previous_day_admission_adult_covid_suspected_20-29` <dbl> …
## $ `previous_day_admission_adult_covid_suspected_20-29_coverage` <dbl> …
## $ `previous_day_admission_adult_covid_suspected_30-39` <dbl> …
## $ `previous_day_admission_adult_covid_suspected_30-39_coverage` <dbl> …
## $ `previous_day_admission_adult_covid_suspected_40-49` <dbl> …
## $ `previous_day_admission_adult_covid_suspected_40-49_coverage` <dbl> …
## $ `previous_day_admission_adult_covid_suspected_50-59` <dbl> …
## $ `previous_day_admission_adult_covid_suspected_50-59_coverage` <dbl> …
## $ `previous_day_admission_adult_covid_suspected_60-69` <dbl> …
## $ `previous_day_admission_adult_covid_suspected_60-69_coverage` <dbl> …
## $ `previous_day_admission_adult_covid_suspected_70-79` <dbl> …
## $ `previous_day_admission_adult_covid_suspected_70-79_coverage` <dbl> …
## $ `previous_day_admission_adult_covid_suspected_80+` <dbl> …
## $ `previous_day_admission_adult_covid_suspected_80+_coverage` <dbl> …
## $ previous_day_admission_adult_covid_suspected_unknown <dbl> …
## $ previous_day_admission_adult_covid_suspected_unknown_coverage <dbl> …
## $ deaths_covid <dbl> …
## $ deaths_covid_coverage <dbl> …
## $ on_hand_supply_therapeutic_a_casirivimab_imdevimab_courses <dbl> …
## $ on_hand_supply_therapeutic_b_bamlanivimab_courses <dbl> …
## $ on_hand_supply_therapeutic_c_bamlanivimab_etesevimab_courses <dbl> …
## $ previous_week_therapeutic_a_casirivimab_imdevimab_courses_used <dbl> …
## $ previous_week_therapeutic_b_bamlanivimab_courses_used <dbl> …
## $ previous_week_therapeutic_c_bamlanivimab_etesevimab_courses_used <dbl> …
## $ icu_patients_confirmed_influenza <dbl> …
## $ icu_patients_confirmed_influenza_coverage <dbl> …
## $ previous_day_admission_influenza_confirmed <dbl> …
## $ previous_day_admission_influenza_confirmed_coverage <dbl> …
## $ previous_day_deaths_covid_and_influenza <dbl> …
## $ previous_day_deaths_covid_and_influenza_coverage <dbl> …
## $ previous_day_deaths_influenza <dbl> …
## $ previous_day_deaths_influenza_coverage <dbl> …
## $ total_patients_hospitalized_confirmed_influenza <dbl> …
## $ total_patients_hospitalized_confirmed_influenza_and_covid <dbl> …
## $ total_patients_hospitalized_confirmed_influenza_and_covid_coverage <dbl> …
## $ total_patients_hospitalized_confirmed_influenza_coverage <dbl> …
## $ all_pediatric_inpatient_bed_occupied <dbl> …
## $ all_pediatric_inpatient_bed_occupied_coverage <dbl> …
## $ all_pediatric_inpatient_beds <dbl> …
## $ all_pediatric_inpatient_beds_coverage <dbl> …
## $ previous_day_admission_pediatric_covid_confirmed_0_4 <dbl> …
## $ previous_day_admission_pediatric_covid_confirmed_0_4_coverage <dbl> …
## $ previous_day_admission_pediatric_covid_confirmed_12_17 <dbl> …
## $ previous_day_admission_pediatric_covid_confirmed_12_17_coverage <dbl> …
## $ previous_day_admission_pediatric_covid_confirmed_5_11 <dbl> …
## $ previous_day_admission_pediatric_covid_confirmed_5_11_coverage <dbl> …
## $ previous_day_admission_pediatric_covid_confirmed_unknown <dbl> …
## $ previous_day_admission_pediatric_covid_confirmed_unknown_coverage <dbl> …
## $ staffed_icu_pediatric_patients_confirmed_covid <dbl> …
## $ staffed_icu_pediatric_patients_confirmed_covid_coverage <dbl> …
## $ staffed_pediatric_icu_bed_occupancy <dbl> …
## $ staffed_pediatric_icu_bed_occupancy_coverage <dbl> …
## $ total_staffed_pediatric_icu_beds <dbl> …
## $ total_staffed_pediatric_icu_beds_coverage <dbl> …## Warning: One or more parsing issues, see `problems()` for details## Rows: 36440 Columns: 101
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (2): Date, Location
## dbl (93): MMWR_week, Distributed, Distributed_Janssen, Distributed_Moderna, ...
## lgl (6): Second_Booster, Administered_Bivalent, Admin_Bivalent_PFR, Admin_B...
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
##
## *** File has been checked for uniqueness by: state date
##
##
## Checking for similarity of: column names
## In reference but not in current:
## In current but not in reference: Administered_Bivalent Admin_Bivalent_PFR Admin_Bivalent_MOD Dist_Bivalent_PFR Dist_Bivalent_MOD
##
## Checking for similarity of: date
## In reference but not in current: 0
## In current but not in reference: 4
##
## Checking for similarity of: state
## In reference but not in current:
## In current but not in reference:
##
##
## ***Differences of at least 1 and at least 1%
##
## [1] date name newValue refValue absDelta pctDelta
## <0 rows> (or 0-length row.names)##
##
## ***Differences of at least 0 and at least 0.1%
##
## [1] state name newValue refValue absDelta pctDelta
## <0 rows> (or 0-length row.names)
##
##
##
## Raw file for vax:
## Rows: 36,440
## Columns: 101
## $ date <date> 2022-09-28, 2022-09-28, 2022-0…
## $ MMWR_week <dbl> 39, 39, 39, 39, 39, 39, 39, 39,…
## $ state <chr> "VA2", "NE", "DE", "WV", "IA", …
## $ Distributed <dbl> 8845320, 4747440, 2836355, 4821…
## $ Distributed_Janssen <dbl> 626900, 151900, 101200, 170200,…
## $ Distributed_Moderna <dbl> 4313180, 1627380, 1078600, 1924…
## $ Distributed_Pfizer <dbl> 3898140, 2963760, 1653155, 2719…
## $ Distributed_Novavax <dbl> 7100, 4400, 3400, 7000, 12500, …
## $ Distributed_Unk_Manuf <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
## $ Dist_Per_100K <dbl> 0, 245421, 291277, 269043, 2530…
## $ Distributed_Per_100k_5Plus <dbl> 0, 263231, 308620, 283773, 2698…
## $ Distributed_Per_100k_12Plus <dbl> 0, 293521, 337739, 309509, 2983…
## $ Distributed_Per_100k_18Plus <dbl> 0, 325539, 368266, 336571, 3288…
## $ Distributed_Per_100k_65Plus <dbl> 0, 1519390, 1501460, 1313760, 1…
## $ vxa <dbl> 7846098, 3451226, 1970701, 2901…
## $ Administered_5Plus <dbl> 7845919, 3437511, 1963888, 2896…
## $ Administered_12Plus <dbl> 7845897, 3306108, 1902046, 2841…
## $ Administered_18Plus <dbl> 7842493, 3086344, 1785352, 2709…
## $ Administered_65Plus <dbl> 4223864, 970237, 620955, 983314…
## $ Administered_Janssen <dbl> 256285, 95847, 62987, 68294, 18…
## $ Administered_Moderna <dbl> 4052239, 1227878, 756774, 12486…
## $ Administered_Pfizer <dbl> 3537422, 2118721, 1148333, 1582…
## $ Administered_Novavax <dbl> 111, 194, 88, 95, 271, 193, 206…
## $ Administered_Unk_Manuf <dbl> 41, 8586, 2519, 2161, 1283, 462…
## $ Admin_Per_100k <dbl> 0, 178413, 202380, 161918, 1748…
## $ Admin_Per_100k_5Plus <dbl> 0, 190599, 213688, 170493, 1856…
## $ Admin_Per_100k_12Plus <dbl> 0, 204408, 226486, 182420, 1987…
## $ Admin_Per_100k_18Plus <dbl> 0, 211635, 231806, 189157, 2064…
## $ Admin_Per_100k_65Plus <dbl> 0, 310518, 328711, 267925, 3140…
## $ Recip_Administered <dbl> 7846098, 3471776, 1946433, 2908…
## $ Administered_Dose1_Recip <dbl> 3545905, 1393418, 835939, 11911…
## $ Administered_Dose1_Pop_Pct <dbl> 0.0, 72.0, 85.8, 66.5, 69.5, 62…
## $ Administered_Dose1_Recip_5Plus <dbl> 3545797, 1385254, 832301, 11881…
## $ Administered_Dose1_Recip_5PlusPop_Pct <dbl> 0.0, 76.8, 90.6, 69.9, 73.6, 66…
## $ Administered_Dose1_Recip_12Plus <dbl> 3545781, 1321056, 800850, 11585…
## $ Administered_Dose1_Recip_12PlusPop_Pct <dbl> 0.0, 81.7, 95.0, 74.4, 78.2, 72…
## $ Administered_Dose1_Recip_18Plus <dbl> 3543854, 1221971, 749018, 10956…
## $ Administered_Dose1_Recip_18PlusPop_Pct <dbl> 0.0, 83.8, 95.0, 76.5, 80.5, 75…
## $ Administered_Dose1_Recip_65Plus <dbl> 1729338, 313542, 218259, 347461…
## $ Administered_Dose1_Recip_65PlusPop_Pct <dbl> 0.0, 95.0, 95.0, 94.7, 95.0, 95…
## $ vxc <dbl> 2988861, 1257663, 694914, 10549…
## $ vxcpoppct <dbl> 0.0, 65.0, 71.4, 58.9, 63.2, 55…
## $ Series_Complete_5Plus <dbl> 2988790, 1254345, 693868, 10537…
## $ Series_Complete_5PlusPop_Pct <dbl> 0.0, 69.5, 75.5, 62.0, 67.3, 59…
## $ Series_Complete_12Plus <dbl> 2988784, 1198619, 668526, 10302…
## $ Series_Complete_12PlusPop_Pct <dbl> 0.0, 74.1, 79.6, 66.1, 71.6, 63…
## $ vxcgte18 <dbl> 2987373, 1108987, 624194, 97492…
## $ vxcgte18pct <dbl> 0.0, 76.0, 81.0, 68.1, 73.7, 66…
## $ vxcgte65 <dbl> 1528522, 292897, 187725, 316675…
## $ vxcgte65pct <dbl> 0.0, 93.7, 95.0, 86.3, 94.7, 88…
## $ Series_Complete_Janssen <dbl> 233980, 89549, 57869, 61920, 16…
## $ Series_Complete_Moderna <dbl> 1467192, 424332, 242886, 432285…
## $ Series_Complete_Pfizer <dbl> 1287661, 741342, 392832, 559945…
## $ Series_Complete_Novavax <dbl> 24, 59, 29, 30, 50, 91, 651, 28…
## $ Series_Complete_Unk_Manuf <dbl> 3, 2100, 861, 589, 643, 1010, 4…
## $ Series_Complete_Janssen_5Plus <dbl> 233974, 89532, 57865, 61903, 16…
## $ Series_Complete_Moderna_5Plus <dbl> 1467169, 421449, 242494, 431420…
## $ Series_Complete_Pfizer_5Plus <dbl> 1287620, 741211, 392619, 559781…
## $ Series_Complete_Unk_Manuf_5Plus <dbl> 3, 2094, 861, 587, 642, 1010, 4…
## $ Series_Complete_Janssen_12Plus <dbl> 233973, 89519, 57858, 61901, 16…
## $ Series_Complete_Moderna_12Plus <dbl> 1467168, 421331, 242451, 431343…
## $ Series_Complete_Pfizer_12Plus <dbl> 1287616, 685669, 367340, 536395…
## $ Series_Complete_Unk_Manuf_12Plus <dbl> 3, 2041, 848, 572, 606, 1004, 4…
## $ Series_Complete_Janssen_18Plus <dbl> 233938, 89441, 57808, 61838, 16…
## $ Series_Complete_Moderna_18Plus <dbl> 1467129, 421072, 242277, 430941…
## $ Series_Complete_Pfizer_18Plus <dbl> 1286279, 596543, 323272, 481581…
## $ Series_Complete_Unk_Manuf_18Plus <dbl> 3, 1877, 808, 533, 554, 996, 43…
## $ Series_Complete_Janssen_65Plus <dbl> 76712, 7058, 10082, 9689, 14679…
## $ Series_Complete_Moderna_65Plus <dbl> 835134, 141612, 77853, 162729, …
## $ Series_Complete_Pfizer_65Plus <dbl> 616669, 143226, 99394, 144029, …
## $ Series_Complete_Unk_Manuf_65Plus <dbl> 2, 993, 393, 223, 163, 373, 227…
## $ Additional_Doses <dbl> 1221492, 673360, 339672, 506667…
## $ Additional_Doses_Vax_Pct <dbl> 40.9, 53.5, 48.9, 48.0, 55.3, 4…
## $ Additional_Doses_5Plus <dbl> 1221487, 673298, 339670, 506630…
## $ Additional_Doses_5Plus_Vax_Pct <dbl> 40.9, 53.7, 49.0, 48.1, 55.4, 4…
## $ Additional_Doses_12Plus <dbl> 1221486, 662455, 335972, 504088…
## $ Additional_Doses_12Plus_Vax_Pct <dbl> 40.9, 55.3, 50.3, 48.9, 56.9, 4…
## $ Additional_Doses_18Plus <dbl> 1221397, 631848, 322036, 491562…
## $ Additional_Doses_18Plus_Vax_Pct <dbl> 40.9, 57.0, 51.6, 50.4, 58.8, 4…
## $ Additional_Doses_50Plus <dbl> 1106281, 400915, 227926, 357348…
## $ Additional_Doses_50Plus_Vax_Pct <dbl> 46.9, 70.2, 63.2, 61.0, 71.9, 6…
## $ Additional_Doses_65Plus <dbl> 795759, 230950, 135191, 218704,…
## $ Additional_Doses_65Plus_Vax_Pct <dbl> 52.1, 78.9, 72.0, 69.1, 80.6, 7…
## $ Additional_Doses_Moderna <dbl> 646719, 255267, 142652, 234519,…
## $ Additional_Doses_Pfizer <dbl> 553511, 410083, 191595, 266805,…
## $ Additional_Doses_Janssen <dbl> 21262, 7100, 5321, 5212, 14337,…
## $ Additional_Doses_Unk_Manuf <dbl> 0, 887, 103, 129, 181, 180, 106…
## $ Second_Booster <lgl> NA, NA, NA, NA, NA, NA, NA, NA,…
## $ Second_Booster_50Plus <dbl> 289139, 153105, 88562, 115683, …
## $ Second_Booster_50Plus_Vax_Pct <dbl> 26.1, 38.2, 38.9, 32.4, 40.6, 3…
## $ Second_Booster_65Plus <dbl> 226537, 105198, 63069, 82695, 2…
## $ Second_Booster_65Plus_Vax_Pct <dbl> 28.5, 45.6, 46.7, 37.8, 49.3, 4…
## $ Second_Booster_Janssen <dbl> 58, 114, 88, 72, 100, 94, 2151,…
## $ Second_Booster_Moderna <dbl> 152100, 62272, 43047, 57960, 14…
## $ Second_Booster_Pfizer <dbl> 141364, 105351, 52049, 67207, 1…
## $ Second_Booster_Unk_Manuf <dbl> 1, 342, 27, 35, 139, 45, 4088, …
## $ Administered_Bivalent <lgl> NA, NA, NA, NA, NA, NA, NA, NA,…
## $ Admin_Bivalent_PFR <lgl> NA, NA, NA, NA, NA, NA, NA, NA,…
## $ Admin_Bivalent_MOD <lgl> NA, NA, NA, NA, NA, NA, NA, NA,…
## $ Dist_Bivalent_PFR <lgl> NA, NA, NA, NA, NA, NA, NA, NA,…
## $ Dist_Bivalent_MOD <lgl> NA, NA, NA, NA, NA, NA, NA, NA,…
##
## Column sums before and after applying filtering rules:
## # A tibble: 3 × 6
## isType tot_cases tot_deaths new_cases new_deaths n
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 before 3.77e+10 5.43e+8 95527960 1036655 57997
## 2 after 3.75e+10 5.40e+8 94432864 1030862 50133
## 3 pctchg 7.08e- 3 4.61e-3 0.0115 0.00559 0.136
##
##
## Processed for cdcDaily:
## Rows: 50,133
## Columns: 6
## $ date <date> 2021-03-11, 2021-12-01, 2020-04-08, 2020-02-04, 2021-09-01…
## $ state <chr> "KS", "ND", "AR", "AR", "ND", "IN", "AR", "AL", "NM", "UT",…
## $ tot_cases <dbl> 297229, 163565, 1071, 0, 118491, 668765, 56199, 547966, 602…
## $ tot_deaths <dbl> 4851, 1907, 18, 0, 1562, 12710, 674, 11530, 8318, 3787, 107…
## $ new_cases <dbl> 0, 589, 78, 0, 536, 487, 547, 154, 1509, 0, 2135, 8, 451, 0…
## $ new_deaths <dbl> 0, 9, 0, 0, 1, 7, 11, 5, 6, 0, 100, 0, 5, 0, 0, 7, 8, 39, 1…
##
## Column sums before and after applying filtering rules:
## # A tibble: 3 × 5
## isType inp hosp_adult hosp_ped n
## <chr> <dbl> <dbl> <dbl> <dbl>
## 1 before 5.19e+7 4.52e+7 1269286 50717
## 2 after 5.16e+7 4.50e+7 1244077 48456
## 3 pctchg 5.41e-3 5.18e-3 0.0199 0.0446
##
##
## Processed for cdcHosp:
## Rows: 48,456
## Columns: 5
## $ date <date> 2021-01-13, 2021-01-13, 2021-01-13, 2021-01-10, 2021-01-09…
## $ state <chr> "DC", "NH", "NV", "RI", "MA", "SD", "RI", "MN", "RI", "SD",…
## $ inp <dbl> 371, 294, 1817, 449, 2075, 247, 483, 1040, 471, 291, 669, 5…
## $ hosp_adult <dbl> 343, 291, 1810, 446, 2051, 244, 479, 1024, 469, 287, 665, 5…
## $ hosp_ped <dbl> 28, 3, 7, 3, 24, 3, 4, 16, 2, 4, 4, 108, 2, 1, 0, 7, 0, 1, …
##
## Column sums before and after applying filtering rules:
## # A tibble: 3 × 9
## isType vxa vxc vxcpoppct vxcgte65 vxcgt…¹ vxcgte18 vxcgt…² n
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 before 4.19e+11 1.72e+11 1527989. 4.35e+10 2.26e+6 1.59e+11 1.80e+6 3.64e+4
## 2 after 2.02e+11 8.31e+10 1278920. 2.11e+10 2.00e+6 7.69e+10 1.52e+6 2.88e+4
## 3 pctchg 5.18e- 1 5.16e- 1 0.163 5.16e- 1 1.14e-1 5.16e- 1 1.54e-1 2.09e-1
## # … with abbreviated variable names ¹vxcgte65pct, ²vxcgte18pct
##
##
## Processed for vax:
## Rows: 28,815
## Columns: 9
## $ date <date> 2022-09-28, 2022-09-28, 2022-09-28, 2022-09-28, 2022-09-2…
## $ state <chr> "NE", "DE", "WV", "IA", "ID", "FL", "ND", "AR", "KY", "MI"…
## $ vxa <dbl> 3451226, 1970701, 2901813, 5515503, 2630561, 39803100, 117…
## $ vxc <dbl> 1257663, 694914, 1054914, 1994024, 989510, 14697269, 43395…
## $ vxcpoppct <dbl> 65.0, 71.4, 58.9, 63.2, 55.4, 68.4, 56.9, 55.8, 58.6, 61.4…
## $ vxcgte65 <dbl> 292897, 187725, 316675, 523635, 257641, 4173533, 105759, 4…
## $ vxcgte65pct <dbl> 93.7, 95.0, 86.3, 94.7, 88.6, 92.8, 88.2, 82.3, 87.9, 89.9…
## $ vxcgte18 <dbl> 1108987, 624194, 974923, 1790031, 891754, 13500109, 393211…
## $ vxcgte18pct <dbl> 76.0, 81.0, 68.1, 73.7, 66.6, 78.3, 67.6, 65.5, 68.7, 70.5…
##
## Integrated per capita data file:
## Rows: 50,346
## Columns: 34
## $ date <date> 2020-01-01, 2020-01-01, 2020-01-01, 2020-01-01, 2020-01-0…
## $ state <chr> "AL", "HI", "IN", "LA", "MN", "MT", "NC", "TX", "AL", "HI"…
## $ tot_cases <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ tot_deaths <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ new_cases <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ new_deaths <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ inp <dbl> NA, 0, 0, NA, 0, 0, 0, 0, NA, 0, 0, NA, 0, 0, 0, 1877, 0, …
## $ hosp_adult <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ hosp_ped <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ vxa <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ vxc <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ vxcpoppct <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ vxcgte65 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ vxcgte65pct <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ vxcgte18 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ vxcgte18pct <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ tcpm <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ tdpm <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ cpm <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ dpm <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ hpm <dbl> NA, 0.0000, 0.0000, NA, 0.0000, 0.0000, 0.0000, 0.0000, NA…
## $ ahpm <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ phpm <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ vxapm <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ vxcpm <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ tcpm7 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ tdpm7 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ cpm7 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ dpm7 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ hpm7 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ ahpm7 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ phpm7 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ vxapm7 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ vxcpm7 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…## Warning in showSRID(uprojargs, format = "PROJ", multiline = "NO", prefer_proj =
## prefer_proj): Discarded datum unknown in Proj4 definition
saveToRDS(cdc_daily_221002, ovrWriteError=FALSE)The function is run to download and process the latest hospitalization data:
# Run for latest data, save as RDS
indivHosp_20221003 <- downloadReadHospitalData(loc="./RInputFiles/Coronavirus/HHS_Hospital_20221003.csv")##
## File ./RInputFiles/Coronavirus/HHS_Hospital_20221003.csv already exists
## File will not be downloaded since ovrWrite is not TRUE## Rows: 260543 Columns: 128
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (11): hospital_pk, state, ccn, hospital_name, address, city, zip, hosp...
## dbl (114): total_beds_7_day_avg, all_adult_hospital_beds_7_day_avg, all_adu...
## lgl (2): is_metro_micro, is_corrected
## date (1): collection_week
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.## Rows: 260,543
## Columns: 128
## $ hospital_pk <chr> …
## $ collection_week <date> …
## $ state <chr> …
## $ ccn <chr> …
## $ hospital_name <chr> …
## $ address <chr> …
## $ city <chr> …
## $ zip <chr> …
## $ hospital_subtype <chr> …
## $ fips_code <chr> …
## $ is_metro_micro <lgl> …
## $ total_beds_7_day_avg <dbl> …
## $ all_adult_hospital_beds_7_day_avg <dbl> …
## $ all_adult_hospital_inpatient_beds_7_day_avg <dbl> …
## $ inpatient_beds_used_7_day_avg <dbl> …
## $ all_adult_hospital_inpatient_bed_occupied_7_day_avg <dbl> …
## $ inpatient_beds_used_covid_7_day_avg <dbl> …
## $ total_adult_patients_hospitalized_confirmed_and_suspected_covid_7_day_avg <dbl> …
## $ total_adult_patients_hospitalized_confirmed_covid_7_day_avg <dbl> …
## $ total_pediatric_patients_hospitalized_confirmed_and_suspected_covid_7_day_avg <dbl> …
## $ total_pediatric_patients_hospitalized_confirmed_covid_7_day_avg <dbl> …
## $ inpatient_beds_7_day_avg <dbl> …
## $ total_icu_beds_7_day_avg <dbl> …
## $ total_staffed_adult_icu_beds_7_day_avg <dbl> …
## $ icu_beds_used_7_day_avg <dbl> …
## $ staffed_adult_icu_bed_occupancy_7_day_avg <dbl> …
## $ staffed_icu_adult_patients_confirmed_and_suspected_covid_7_day_avg <dbl> …
## $ staffed_icu_adult_patients_confirmed_covid_7_day_avg <dbl> …
## $ total_patients_hospitalized_confirmed_influenza_7_day_avg <dbl> …
## $ icu_patients_confirmed_influenza_7_day_avg <dbl> …
## $ total_patients_hospitalized_confirmed_influenza_and_covid_7_day_avg <dbl> …
## $ total_beds_7_day_sum <dbl> …
## $ all_adult_hospital_beds_7_day_sum <dbl> …
## $ all_adult_hospital_inpatient_beds_7_day_sum <dbl> …
## $ inpatient_beds_used_7_day_sum <dbl> …
## $ all_adult_hospital_inpatient_bed_occupied_7_day_sum <dbl> …
## $ inpatient_beds_used_covid_7_day_sum <dbl> …
## $ total_adult_patients_hospitalized_confirmed_and_suspected_covid_7_day_sum <dbl> …
## $ total_adult_patients_hospitalized_confirmed_covid_7_day_sum <dbl> …
## $ total_pediatric_patients_hospitalized_confirmed_and_suspected_covid_7_day_sum <dbl> …
## $ total_pediatric_patients_hospitalized_confirmed_covid_7_day_sum <dbl> …
## $ inpatient_beds_7_day_sum <dbl> …
## $ total_icu_beds_7_day_sum <dbl> …
## $ total_staffed_adult_icu_beds_7_day_sum <dbl> …
## $ icu_beds_used_7_day_sum <dbl> …
## $ staffed_adult_icu_bed_occupancy_7_day_sum <dbl> …
## $ staffed_icu_adult_patients_confirmed_and_suspected_covid_7_day_sum <dbl> …
## $ staffed_icu_adult_patients_confirmed_covid_7_day_sum <dbl> …
## $ total_patients_hospitalized_confirmed_influenza_7_day_sum <dbl> …
## $ icu_patients_confirmed_influenza_7_day_sum <dbl> …
## $ total_patients_hospitalized_confirmed_influenza_and_covid_7_day_sum <dbl> …
## $ total_beds_7_day_coverage <dbl> …
## $ all_adult_hospital_beds_7_day_coverage <dbl> …
## $ all_adult_hospital_inpatient_beds_7_day_coverage <dbl> …
## $ inpatient_beds_used_7_day_coverage <dbl> …
## $ all_adult_hospital_inpatient_bed_occupied_7_day_coverage <dbl> …
## $ inpatient_beds_used_covid_7_day_coverage <dbl> …
## $ total_adult_patients_hospitalized_confirmed_and_suspected_covid_7_day_coverage <dbl> …
## $ total_adult_patients_hospitalized_confirmed_covid_7_day_coverage <dbl> …
## $ total_pediatric_patients_hospitalized_confirmed_and_suspected_covid_7_day_coverage <dbl> …
## $ total_pediatric_patients_hospitalized_confirmed_covid_7_day_coverage <dbl> …
## $ inpatient_beds_7_day_coverage <dbl> …
## $ total_icu_beds_7_day_coverage <dbl> …
## $ total_staffed_adult_icu_beds_7_day_coverage <dbl> …
## $ icu_beds_used_7_day_coverage <dbl> …
## $ staffed_adult_icu_bed_occupancy_7_day_coverage <dbl> …
## $ staffed_icu_adult_patients_confirmed_and_suspected_covid_7_day_coverage <dbl> …
## $ staffed_icu_adult_patients_confirmed_covid_7_day_coverage <dbl> …
## $ total_patients_hospitalized_confirmed_influenza_7_day_coverage <dbl> …
## $ icu_patients_confirmed_influenza_7_day_coverage <dbl> …
## $ total_patients_hospitalized_confirmed_influenza_and_covid_7_day_coverage <dbl> …
## $ previous_day_admission_adult_covid_confirmed_7_day_sum <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_18-19_7_day_sum` <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_20-29_7_day_sum` <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_30-39_7_day_sum` <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_40-49_7_day_sum` <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_50-59_7_day_sum` <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_60-69_7_day_sum` <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_70-79_7_day_sum` <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_80+_7_day_sum` <dbl> …
## $ previous_day_admission_adult_covid_confirmed_unknown_7_day_sum <dbl> …
## $ previous_day_admission_pediatric_covid_confirmed_7_day_sum <dbl> …
## $ previous_day_covid_ED_visits_7_day_sum <dbl> …
## $ previous_day_admission_adult_covid_suspected_7_day_sum <dbl> …
## $ `previous_day_admission_adult_covid_suspected_18-19_7_day_sum` <dbl> …
## $ `previous_day_admission_adult_covid_suspected_20-29_7_day_sum` <dbl> …
## $ `previous_day_admission_adult_covid_suspected_30-39_7_day_sum` <dbl> …
## $ `previous_day_admission_adult_covid_suspected_40-49_7_day_sum` <dbl> …
## $ `previous_day_admission_adult_covid_suspected_50-59_7_day_sum` <dbl> …
## $ `previous_day_admission_adult_covid_suspected_60-69_7_day_sum` <dbl> …
## $ `previous_day_admission_adult_covid_suspected_70-79_7_day_sum` <dbl> …
## $ `previous_day_admission_adult_covid_suspected_80+_7_day_sum` <dbl> …
## $ previous_day_admission_adult_covid_suspected_unknown_7_day_sum <dbl> …
## $ previous_day_admission_pediatric_covid_suspected_7_day_sum <dbl> …
## $ previous_day_total_ED_visits_7_day_sum <dbl> …
## $ previous_day_admission_influenza_confirmed_7_day_sum <dbl> …
## $ geocoded_hospital_address <chr> …
## $ hhs_ids <chr> …
## $ previous_day_admission_adult_covid_confirmed_7_day_coverage <dbl> …
## $ previous_day_admission_pediatric_covid_confirmed_7_day_coverage <dbl> …
## $ previous_day_admission_adult_covid_suspected_7_day_coverage <dbl> …
## $ previous_day_admission_pediatric_covid_suspected_7_day_coverage <dbl> …
## $ previous_week_personnel_covid_vaccinated_doses_administered_7_day <dbl> …
## $ total_personnel_covid_vaccinated_doses_none_7_day <dbl> …
## $ total_personnel_covid_vaccinated_doses_one_7_day <dbl> …
## $ total_personnel_covid_vaccinated_doses_all_7_day <dbl> …
## $ previous_week_patients_covid_vaccinated_doses_one_7_day <dbl> …
## $ previous_week_patients_covid_vaccinated_doses_all_7_day <dbl> …
## $ is_corrected <lgl> …
## $ all_pediatric_inpatient_bed_occupied_7_day_avg <dbl> …
## $ all_pediatric_inpatient_bed_occupied_7_day_coverage <dbl> …
## $ all_pediatric_inpatient_bed_occupied_7_day_sum <dbl> …
## $ all_pediatric_inpatient_beds_7_day_avg <dbl> …
## $ all_pediatric_inpatient_beds_7_day_coverage <dbl> …
## $ all_pediatric_inpatient_beds_7_day_sum <dbl> …
## $ previous_day_admission_pediatric_covid_confirmed_0_4_7_day_sum <dbl> …
## $ previous_day_admission_pediatric_covid_confirmed_12_17_7_day_sum <dbl> …
## $ previous_day_admission_pediatric_covid_confirmed_5_11_7_day_sum <dbl> …
## $ previous_day_admission_pediatric_covid_confirmed_unknown_7_day_sum <dbl> …
## $ staffed_icu_pediatric_patients_confirmed_covid_7_day_avg <dbl> …
## $ staffed_icu_pediatric_patients_confirmed_covid_7_day_coverage <dbl> …
## $ staffed_icu_pediatric_patients_confirmed_covid_7_day_sum <dbl> …
## $ staffed_pediatric_icu_bed_occupancy_7_day_avg <dbl> …
## $ staffed_pediatric_icu_bed_occupancy_7_day_coverage <dbl> …
## $ staffed_pediatric_icu_bed_occupancy_7_day_sum <dbl> …
## $ total_staffed_pediatric_icu_beds_7_day_avg <dbl> …
## $ total_staffed_pediatric_icu_beds_7_day_coverage <dbl> …
## $ total_staffed_pediatric_icu_beds_7_day_sum <dbl> …
##
## Hospital Subtype Counts:
## # A tibble: 4 × 2
## hospital_subtype n
## <chr> <int>
## 1 Childrens Hospitals 4909
## 2 Critical Access Hospitals 69937
## 3 Long Term 17869
## 4 Short Term 167828
##
## Records other than 50 states and DC
## # A tibble: 5 × 2
## state n
## <chr> <int>
## 1 AS 52
## 2 GU 102
## 3 MP 40
## 4 PR 2766
## 5 VI 104
##
## Record types for key metrics
## # A tibble: 10 × 5
## name `NA` Posit…¹ Value…² Total
## <chr> <int> <int> <int> <int>
## 1 all_adult_hospital_beds_7_day_avg 71194 188847 502 260543
## 2 all_adult_hospital_inpatient_bed_occupied_7_day… 131 238657 21755 260543
## 3 icu_beds_used_7_day_avg 56 228646 31841 260543
## 4 inpatient_beds_7_day_avg 46 259479 1018 260543
## 5 inpatient_beds_used_7_day_avg 35 239484 21024 260543
## 6 inpatient_beds_used_covid_7_day_avg 26 173367 87150 260543
## 7 staffed_icu_adult_patients_confirmed_and_suspec… 169 174858 85516 260543
## 8 total_adult_patients_hospitalized_confirmed_and… 127 173350 87066 260543
## 9 total_beds_7_day_avg 69419 190826 298 260543
## 10 total_icu_beds_7_day_avg 58 246878 13607 260543
## # … with abbreviated variable names ¹Positive, ²`Value -999999`## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
saveToRDS(indivHosp_20221003, ovrWriteError=FALSE)Post-processing is run, including hospital summaries:
# Create pivoted burden data
burdenPivotList_221002 <- postProcessCDCDaily(cdc_daily_221002,
dataThruLabel="Sep 2022",
keyDatesBurden=c("2022-09-30", "2022-03-31",
"2021-09-30", "2021-03-31"
),
keyDatesVaccine=c("2022-09-28", "2022-03-31",
"2021-09-30", "2021-03-31"
),
returnData=TRUE
)## Joining, by = "state"##
## *** File has been checked for uniqueness by: state date name## Warning: Removed 24 row(s) containing missing values (geom_path).
## Warning: Removed 24 rows containing missing values (position_stack).
## Warning: Removed 24 rows containing missing values (position_stack).
## Warning: Removed 9 row(s) containing missing values (geom_path).
# Create hospitalized per capita data
hospPerCap_221002 <- hospAgePerCapita(readFromRDS("dfStateAgeBucket2019"),
lst=burdenPivotList_221002,
popVar="pop2019",
excludeState=c(),
cumStartDate="2020-07-15"
)## Warning: Removed 18 row(s) containing missing values (geom_path).
burdenPivotList_221002$hospAge %>%
group_by(adultPed, confSusp, age, name) %>%
summarize(value=sum(value, na.rm=TRUE), n=n(), .groups="drop")## # A tibble: 18 × 6
## adultPed confSusp age name value n
## <chr> <chr> <chr> <chr> <dbl> <int>
## 1 adult confirmed 0-19 previous_day_admission_adult_covid_con… 4.77e4 50717
## 2 adult confirmed 20-29 previous_day_admission_adult_covid_con… 2.90e5 50717
## 3 adult confirmed 30-39 previous_day_admission_adult_covid_con… 4.19e5 50717
## 4 adult confirmed 40-49 previous_day_admission_adult_covid_con… 5.02e5 50717
## 5 adult confirmed 50-59 previous_day_admission_adult_covid_con… 8.01e5 50717
## 6 adult confirmed 60-69 previous_day_admission_adult_covid_con… 1.05e6 50717
## 7 adult confirmed 70-79 previous_day_admission_adult_covid_con… 1.06e6 50717
## 8 adult confirmed 80+ previous_day_admission_adult_covid_con… 9.58e5 50717
## 9 adult suspected 0-19 previous_day_admission_adult_covid_sus… 3.91e4 50717
## 10 adult suspected 20-29 previous_day_admission_adult_covid_sus… 2.61e5 50717
## 11 adult suspected 30-39 previous_day_admission_adult_covid_sus… 3.43e5 50717
## 12 adult suspected 40-49 previous_day_admission_adult_covid_sus… 3.47e5 50717
## 13 adult suspected 50-59 previous_day_admission_adult_covid_sus… 5.48e5 50717
## 14 adult suspected 60-69 previous_day_admission_adult_covid_sus… 7.55e5 50717
## 15 adult suspected 70-79 previous_day_admission_adult_covid_sus… 7.36e5 50717
## 16 adult suspected 80+ previous_day_admission_adult_covid_sus… 6.70e5 50717
## 17 ped confirmed 0-19 previous_day_admission_pediatric_covid… 1.73e5 50717
## 18 ped suspected 0-19 previous_day_admission_pediatric_covid… 3.88e5 50717saveToRDS(burdenPivotList_221002, ovrWriteError=FALSE)
saveToRDS(hospPerCap_221002, ovrWriteError=FALSE)Peaks and valleys of key metrics are also updated:
peakValleyCDCDaily(cdc_daily_221002)## Warning: Removed 6 row(s) containing missing values (geom_path).
## Warning: Removed 6 row(s) containing missing values (geom_path).
## Warning: Removed 6 row(s) containing missing values (geom_path).
## Warning: Removed 20 row(s) containing missing values (geom_path).
## Warning: Removed 20 row(s) containing missing values (geom_path).
## # A tibble: 8,040 × 8
## date state vxa vxc vxa_isPeak vxc_isPeak vxa_isValley vxc_isValley
## <date> <chr> <dbl> <dbl> <lgl> <lgl> <lgl> <lgl>
## 1 2020-12-01 CA NA NA FALSE FALSE FALSE FALSE
## 2 2020-12-01 FL NA NA FALSE FALSE FALSE FALSE
## 3 2020-12-01 GA NA NA FALSE FALSE FALSE FALSE
## 4 2020-12-01 IL NA NA FALSE FALSE FALSE FALSE
## 5 2020-12-01 MI NA NA FALSE FALSE FALSE FALSE
## 6 2020-12-01 NC NA NA FALSE FALSE FALSE FALSE
## 7 2020-12-01 NJ NA NA FALSE FALSE FALSE FALSE
## 8 2020-12-01 NY NA NA FALSE FALSE FALSE FALSE
## 9 2020-12-01 OH NA NA FALSE FALSE FALSE FALSE
## 10 2020-12-01 PA NA NA FALSE FALSE FALSE FALSE
## # … with 8,030 more rows
## # ℹ Use `print(n = ...)` to see more rowsA function is written for the hospital data availability checks:
checkHospitalDataComplete <- function(df1=NULL,
df2=NULL,
dfAll=NULL,
lab1="DF1",
lab2="DF2",
trendBreaks=c(),
makeP1=TRUE,
makeP2=TRUE,
returnData=FALSE
) {
# FUNCTION ARGUMENTS:
# df1: the first data frame (NULL means integrated frame passed as dfAll)
# df2: the second data frame (NULL means integrated frame passed as dfAll)
# dfAll: integrated data frame from previous iteration of function (will ignore df1 and df2)
# lab1: plot label for the first data frame
# lab2: plot label for the second data frame
# trendBreaks: character vector of trend break dates of form YYYY-MM-DD - if c(), no trend brek vlines plotted
# makeP1: boolean, should the first plot be created and printed?
# makeP2: boolean, should the first plot be created and printed?
# returnData: boolean, should dfAll be returned?
# Temporary function to aggregate data
tempCountComplete <- function(df) {
df %>%
select(hospital_pk, collection_week, all_of(names(hhsMapper))) %>%
colRenamer(vecRename=hhsMapper) %>%
pivot_longer(-c(hospital_pk, collection_week)) %>%
filter(!is.na(value), value>0) %>%
count(collection_week, name)
}
# Create or use passed data
if(is.null(dfAll)) {
if(is.null(df1) | is.null(df2)) stop("dfAll not passed requires both df1 and df2 to be passed\n")
dfAll <- bind_rows(tempCountComplete(df1), tempCountComplete(df2), .id="src")
} else {
if(!is.null(df1) | !is.null(df2)) warning("dfAll passed and will be used; df1 and/or df2 ignored\n")
}
# Plot data completeness - hospitals reporting >0 on metric by week
if(isTRUE(makeP1)) {
p1 <- dfAll %>%
select(collection_week, name) %>%
unique() %>%
bind_rows(., ., .id="src") %>%
full_join(dfAll, by=c("src", "collection_week", "name")) %>%
mutate(src=c("1"=lab1, "2"=lab2)[src]) %>%
mutate(n=ifelse(is.na(n), 0, n)) %>%
ggplot(aes(x=collection_week, y=n)) +
geom_line(aes(group=src, color=src)) +
facet_wrap(~name) +
labs(title="Number of hospitals in US reporting >0 on metric by week",
x=NULL,
y="# Hospitals Reporting > 0"
) +
scale_color_discrete("Data Source:")
if(length(trendBreaks) > 0) {
p1 <- p1 + geom_vline(xintercept=as.Date(all_of(trendBreaks)), lty=2) +
labs(subtitle=paste0("Trend break dashed lines at ",
paste0(all_of(trendBreaks), collapse=" and ")
)
)
}
print(p1)
}
# Plot data completeness - showing difference in reported data and trend break dates
if(isTRUE(makeP2)) {
p2 <- dfAll %>%
select(collection_week, name) %>%
unique() %>%
bind_rows(., ., .id="src") %>%
full_join(dfAll, by=c("src", "collection_week", "name")) %>%
mutate(src=c("1"=lab1, "2"=lab2)[src]) %>%
mutate(n=ifelse(is.na(n), 0, n)) %>%
group_by(collection_week, name) %>%
summarize(delta=sum(ifelse(src==lab1, n, 0)-ifelse(src!=lab1, n, 0)), .groups="drop") %>%
ggplot(aes(x=collection_week, y=delta)) +
geom_line() +
geom_point(data=~filter(., delta != 0),
aes(color=case_when(delta>=0 ~ "darkgreen", TRUE ~ "red"))
) +
geom_hline(yintercept=0, lty=2) +
scale_color_identity(NULL) +
facet_wrap(~name) +
labs(title="Delta in Number of hospitals in US reporting >0 on metric by week",
x=NULL,
y="Delta in # Hospitals Reporting > 0"
)
if(length(trendBreaks) > 0) {
p2 <- p2 + geom_vline(xintercept=as.Date(all_of(trendBreaks)), lty=2) +
labs(subtitle=paste0("Trend break dashed lines at ",
paste0(all_of(trendBreaks), collapse=" and ")
)
)
}
print(p2)
}
if(isTRUE(returnData)) return(dfAll)
}
# Create the data
dfTemp <- checkHospitalDataComplete(df1=readFromRDS("indivHosp_20221003"),
df2=readFromRDS("indivHosp_20220704"),
makeP1=FALSE,
makeP2=FALSE,
returnData=TRUE
)
# Create the first plot
checkHospitalDataComplete(dfAll=dfTemp,
lab1="OCT-2022",
lab2="JUL-2022",
makeP2=FALSE,
trendBreaks=c("2021-09-25", "2022-06-24")
)
# Create the second plot
checkHospitalDataComplete(dfAll=dfTemp,
lab1="OCT-2022",
lab2="JUL-2022",
makeP1=FALSE,
trendBreaks=c("2021-09-25", "2022-06-24")
)
The discontinuity issues due to occasional minor changes between positive and negative is resolved by using points colored by positive/negative and a solid line
Hospital data are pieced together as needed:
# Create modified hospital data
multiSourceHosp_20221002 <- multiSourceDataCombine(list(readFromRDS("indivHosp_20220704"),
readFromRDS("indivHosp_20221003")
),
timeVec=as.Date("2022-01-01")
)The updated hospital data are then plotted:
# Run hospital plots
modStateHosp_20221002 <- hospitalCapacityCDCDaily(multiSourceHosp_20221002,
plotSub="Aug 2020 to Sep 2022\nOld data used pre-2022"
)
Data From 2022-11-02
The latest CDC case, hospitalization, and death data are downloaded and processed:
readList <- list("cdcDaily"="./RInputFiles/Coronavirus/CDC_dc_downloaded_221102.csv",
"cdcHosp"="./RInputFiles/Coronavirus/CDC_h_downloaded_221102.csv",
"vax"="./RInputFiles/Coronavirus/vaxData_downloaded_221102.csv"
)
compareList <- list("cdcDaily"=readFromRDS("cdc_daily_221002")$dfRaw$cdcDaily,
"cdcHosp"=readFromRDS("cdc_daily_221002")$dfRaw$cdcHosp,
"vax"=readFromRDS("cdc_daily_221002")$dfRaw$vax
)
cdc_daily_221102 <- readRunCDCDaily(thruLabel="Oct 31, 2022",
downloadTo=lapply(readList, FUN=function(x) if(file.exists(x)) NA else x),
readFrom=readList,
compareFile=compareList,
writeLog=NULL,
useClusters=readFromRDS("cdc_daily_210528")$useClusters,
weightedMeanAggs=c("tcpm7", "tdpm7", "cpm7", "dpm7", "hpm7",
"vxcpm7", "vxcgte65pct"
),
skipAssessmentPlots=FALSE,
brewPalette="Paired"
)## Rows: 60060 Columns: 15
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (5): submission_date, state, created_at, consent_cases, consent_deaths
## dbl (10): tot_cases, conf_cases, prob_cases, new_case, pnew_case, tot_death,...
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.##
## *** File has been checked for uniqueness by: state date
##
##
## Checking for similarity of: column names
## In reference but not in current:
## In current but not in reference:
##
## Checking for similarity of: date
## In reference but not in current: 0
## In current but not in reference: 18
##
## Checking for similarity of: state
## In reference but not in current:
## In current but not in reference:
##
##
## ***Differences of at least 5 and at least 5%
##
## date name newValue refValue absDelta pctDelta
## 1 2022-09-25 new_deaths 92 38 54 0.83076923
## 2 2022-09-24 new_deaths 111 81 30 0.31250000
## 3 2022-09-18 new_deaths 83 68 15 0.19867550
## 4 2022-09-26 new_deaths 233 198 35 0.16241299
## 5 2022-09-17 new_deaths 103 89 14 0.14583333
## 6 2022-09-30 new_deaths 370 321 49 0.14182344
## 7 2022-09-10 new_deaths 140 122 18 0.13740458
## 8 2022-08-28 new_deaths 131 117 14 0.11290323
## 9 2022-09-27 new_deaths 470 429 41 0.09121246
## 10 2022-09-23 new_deaths 355 327 28 0.08211144
## 11 2022-08-21 new_deaths 132 122 10 0.07874016
## 12 2022-09-19 new_deaths 228 211 17 0.07744875
## 13 2022-09-05 new_deaths 114 106 8 0.07272727
## 14 2022-08-20 new_deaths 149 139 10 0.06944444
## 15 2022-09-29 new_deaths 605 570 35 0.05957447
## 16 2022-08-27 new_deaths 172 163 9 0.05373134
## 17 2022-09-03 new_deaths 122 116 6 0.05042017
## 18 2022-09-30 new_cases 49011 46391 2620 0.05492547
## 19 2022-09-28 new_cases 67106 63713 3393 0.05187320
##
##
## ***Differences of at least 0 and at least 0.1%
##
## state name newValue refValue absDelta pctDelta
## 1 NC new_deaths 26857 26531 326 0.012212482
## 2 ND new_deaths 2375 2364 11 0.004642330
## 3 FL new_deaths 81756 81441 315 0.003860365
## 4 AL new_deaths 20498 20423 75 0.003665600
## 5 NYC new_deaths 41934 41977 43 0.001024895
## 6 NC new_cases 3207665 3200475 7190 0.002244021
## 7 SC new_cases 1711020 1708074 2946 0.001723264
##
##
##
## Raw file for cdcDaily:
## Rows: 60,060
## Columns: 15
## $ date <date> 2021-03-11, 2021-12-01, 2022-01-02, 2021-11-22, 2022-0…
## $ state <chr> "KS", "ND", "AS", "AL", "AK", "RMI", "ND", "PR", "PW", …
## $ tot_cases <dbl> 297229, 163565, 11, 841461, 251425, 0, 173, 173967, 0, …
## $ conf_cases <dbl> 241035, 135705, NA, 620483, NA, 0, NA, 144788, NA, NA, …
## $ prob_cases <dbl> 56194, 27860, NA, 220978, NA, 0, NA, 29179, NA, NA, 0, …
## $ new_cases <dbl> 0, 589, 0, 703, 0, 0, 14, 667, 0, 1509, 0, 28, 2293, 59…
## $ pnew_case <dbl> 0, 220, 0, 357, 0, 0, NA, 274, 0, 0, 0, 5, 552, 1266, 7…
## $ tot_deaths <dbl> 4851, 1907, 0, 16377, 1252, 0, 3, 2911, 0, 8318, 3787, …
## $ conf_death <dbl> NA, NA, NA, 12727, NA, 0, NA, 2482, NA, NA, 3635, 1601,…
## $ prob_death <dbl> NA, NA, NA, 3650, NA, 0, NA, 429, NA, NA, 152, 366, 0, …
## $ new_deaths <dbl> 0, 9, 0, 7, 0, 0, 0, 8, 0, 6, 0, 0, 0, 8, 0, 4, 0, 0, 1…
## $ pnew_death <dbl> 0, 0, 0, 3, 0, 0, NA, 3, 0, 0, 0, 0, 0, -1, 0, 0, 0, 0,…
## $ created_at <chr> "03/12/2021 03:20:13 PM", "12/02/2021 02:35:20 PM", "01…
## $ consent_cases <chr> "Agree", "Agree", NA, "Agree", "N/A", "Agree", "Agree",…
## $ consent_deaths <chr> "N/A", "Not agree", NA, "Agree", "N/A", "Agree", "Not a…## Rows: 52391 Columns: 135
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (1): state
## dbl (132): critical_staffing_shortage_today_yes, critical_staffing_shortage...
## lgl (1): geocoded_state
## date (1): date
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
##
## *** File has been checked for uniqueness by: state date
##
##
## Checking for similarity of: column names
## In reference but not in current:
## In current but not in reference:
##
## Checking for similarity of: date
## In reference but not in current: 0
## In current but not in reference: 31
##
## Checking for similarity of: state
## In reference but not in current:
## In current but not in reference:
##
##
## ***Differences of at least 5 and at least 5%
##
## date name newValue refValue absDelta pctDelta
## 1 2020-08-02 hosp_ped 6791 4561 2230 0.3928823
##
##
## ***Differences of at least 0 and at least 0.1%
##
## state name newValue refValue absDelta pctDelta
## 1 NV inp 669396 670631 1235 0.001843246
## 2 NH hosp_ped 1328 1282 46 0.035249042
## 3 WV hosp_ped 6407 6206 201 0.031871878
## 4 ME hosp_ped 2722 2675 47 0.017417084
## 5 MA hosp_ped 14134 13890 244 0.017413645
## 6 NV hosp_ped 5928 5831 97 0.016498002
## 7 VI hosp_ped 128 130 2 0.015503876
## 8 SC hosp_ped 10059 9955 104 0.010392725
## 9 AR hosp_ped 14079 13965 114 0.008130081
## 10 AZ hosp_ped 29543 29312 231 0.007849800
## 11 TN hosp_ped 24231 24060 171 0.007082065
## 12 AL hosp_ped 22626 22485 141 0.006251247
## 13 VT hosp_ped 658 654 4 0.006097561
## 14 UT hosp_ped 11316 11255 61 0.005405166
## 15 NM hosp_ped 8584 8545 39 0.004553681
## 16 IN hosp_ped 18602 18682 80 0.004291385
## 17 KS hosp_ped 5308 5288 20 0.003775009
## 18 PR hosp_ped 25141 25055 86 0.003426568
## 19 CO hosp_ped 23478 23402 76 0.003242321
## 20 FL hosp_ped 97891 97593 298 0.003048843
## 21 MS hosp_ped 12979 13017 38 0.002923527
## 22 KY hosp_ped 22910 22845 65 0.002841220
## 23 NJ hosp_ped 20956 20999 43 0.002049815
## 24 IA hosp_ped 8413 8430 17 0.002018643
## 25 PA hosp_ped 59345 59227 118 0.001990352
## 26 GA hosp_ped 57694 57605 89 0.001543812
## 27 OK hosp_ped 28563 28528 35 0.001226113
## 28 MO hosp_ped 43011 43059 48 0.001115371
## 29 IL hosp_ped 47538 47488 50 0.001052344
## 30 OR hosp_ped 12762 12749 13 0.001019168
## 31 NV hosp_adult 616917 618249 1332 0.002156795
## 32 WV hosp_adult 345523 345909 386 0.001116523
##
##
##
## Raw file for cdcHosp:
## Rows: 52,391
## Columns: 135
## $ state <chr> …
## $ date <date> …
## $ critical_staffing_shortage_today_yes <dbl> …
## $ critical_staffing_shortage_today_no <dbl> …
## $ critical_staffing_shortage_today_not_reported <dbl> …
## $ critical_staffing_shortage_anticipated_within_week_yes <dbl> …
## $ critical_staffing_shortage_anticipated_within_week_no <dbl> …
## $ critical_staffing_shortage_anticipated_within_week_not_reported <dbl> …
## $ hospital_onset_covid <dbl> …
## $ hospital_onset_covid_coverage <dbl> …
## $ inpatient_beds <dbl> …
## $ inpatient_beds_coverage <dbl> …
## $ inpatient_beds_used <dbl> …
## $ inpatient_beds_used_coverage <dbl> …
## $ inp <dbl> …
## $ inpatient_beds_used_covid_coverage <dbl> …
## $ previous_day_admission_adult_covid_confirmed <dbl> …
## $ previous_day_admission_adult_covid_confirmed_coverage <dbl> …
## $ previous_day_admission_adult_covid_suspected <dbl> …
## $ previous_day_admission_adult_covid_suspected_coverage <dbl> …
## $ previous_day_admission_pediatric_covid_confirmed <dbl> …
## $ previous_day_admission_pediatric_covid_confirmed_coverage <dbl> …
## $ previous_day_admission_pediatric_covid_suspected <dbl> …
## $ previous_day_admission_pediatric_covid_suspected_coverage <dbl> …
## $ staffed_adult_icu_bed_occupancy <dbl> …
## $ staffed_adult_icu_bed_occupancy_coverage <dbl> …
## $ staffed_icu_adult_patients_confirmed_and_suspected_covid <dbl> …
## $ staffed_icu_adult_patients_confirmed_and_suspected_covid_coverage <dbl> …
## $ staffed_icu_adult_patients_confirmed_covid <dbl> …
## $ staffed_icu_adult_patients_confirmed_covid_coverage <dbl> …
## $ hosp_adult <dbl> …
## $ total_adult_patients_hospitalized_confirmed_and_suspected_covid_coverage <dbl> …
## $ total_adult_patients_hospitalized_confirmed_covid <dbl> …
## $ total_adult_patients_hospitalized_confirmed_covid_coverage <dbl> …
## $ hosp_ped <dbl> …
## $ total_pediatric_patients_hospitalized_confirmed_and_suspected_covid_coverage <dbl> …
## $ total_pediatric_patients_hospitalized_confirmed_covid <dbl> …
## $ total_pediatric_patients_hospitalized_confirmed_covid_coverage <dbl> …
## $ total_staffed_adult_icu_beds <dbl> …
## $ total_staffed_adult_icu_beds_coverage <dbl> …
## $ inpatient_beds_utilization <dbl> …
## $ inpatient_beds_utilization_coverage <dbl> …
## $ inpatient_beds_utilization_numerator <dbl> …
## $ inpatient_beds_utilization_denominator <dbl> …
## $ percent_of_inpatients_with_covid <dbl> …
## $ percent_of_inpatients_with_covid_coverage <dbl> …
## $ percent_of_inpatients_with_covid_numerator <dbl> …
## $ percent_of_inpatients_with_covid_denominator <dbl> …
## $ inpatient_bed_covid_utilization <dbl> …
## $ inpatient_bed_covid_utilization_coverage <dbl> …
## $ inpatient_bed_covid_utilization_numerator <dbl> …
## $ inpatient_bed_covid_utilization_denominator <dbl> …
## $ adult_icu_bed_covid_utilization <dbl> …
## $ adult_icu_bed_covid_utilization_coverage <dbl> …
## $ adult_icu_bed_covid_utilization_numerator <dbl> …
## $ adult_icu_bed_covid_utilization_denominator <dbl> …
## $ adult_icu_bed_utilization <dbl> …
## $ adult_icu_bed_utilization_coverage <dbl> …
## $ adult_icu_bed_utilization_numerator <dbl> …
## $ adult_icu_bed_utilization_denominator <dbl> …
## $ geocoded_state <lgl> …
## $ `previous_day_admission_adult_covid_confirmed_18-19` <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_18-19_coverage` <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_20-29` <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_20-29_coverage` <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_30-39` <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_30-39_coverage` <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_40-49` <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_40-49_coverage` <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_50-59` <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_50-59_coverage` <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_60-69` <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_60-69_coverage` <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_70-79` <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_70-79_coverage` <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_80+` <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_80+_coverage` <dbl> …
## $ previous_day_admission_adult_covid_confirmed_unknown <dbl> …
## $ previous_day_admission_adult_covid_confirmed_unknown_coverage <dbl> …
## $ `previous_day_admission_adult_covid_suspected_18-19` <dbl> …
## $ `previous_day_admission_adult_covid_suspected_18-19_coverage` <dbl> …
## $ `previous_day_admission_adult_covid_suspected_20-29` <dbl> …
## $ `previous_day_admission_adult_covid_suspected_20-29_coverage` <dbl> …
## $ `previous_day_admission_adult_covid_suspected_30-39` <dbl> …
## $ `previous_day_admission_adult_covid_suspected_30-39_coverage` <dbl> …
## $ `previous_day_admission_adult_covid_suspected_40-49` <dbl> …
## $ `previous_day_admission_adult_covid_suspected_40-49_coverage` <dbl> …
## $ `previous_day_admission_adult_covid_suspected_50-59` <dbl> …
## $ `previous_day_admission_adult_covid_suspected_50-59_coverage` <dbl> …
## $ `previous_day_admission_adult_covid_suspected_60-69` <dbl> …
## $ `previous_day_admission_adult_covid_suspected_60-69_coverage` <dbl> …
## $ `previous_day_admission_adult_covid_suspected_70-79` <dbl> …
## $ `previous_day_admission_adult_covid_suspected_70-79_coverage` <dbl> …
## $ `previous_day_admission_adult_covid_suspected_80+` <dbl> …
## $ `previous_day_admission_adult_covid_suspected_80+_coverage` <dbl> …
## $ previous_day_admission_adult_covid_suspected_unknown <dbl> …
## $ previous_day_admission_adult_covid_suspected_unknown_coverage <dbl> …
## $ deaths_covid <dbl> …
## $ deaths_covid_coverage <dbl> …
## $ on_hand_supply_therapeutic_a_casirivimab_imdevimab_courses <dbl> …
## $ on_hand_supply_therapeutic_b_bamlanivimab_courses <dbl> …
## $ on_hand_supply_therapeutic_c_bamlanivimab_etesevimab_courses <dbl> …
## $ previous_week_therapeutic_a_casirivimab_imdevimab_courses_used <dbl> …
## $ previous_week_therapeutic_b_bamlanivimab_courses_used <dbl> …
## $ previous_week_therapeutic_c_bamlanivimab_etesevimab_courses_used <dbl> …
## $ icu_patients_confirmed_influenza <dbl> …
## $ icu_patients_confirmed_influenza_coverage <dbl> …
## $ previous_day_admission_influenza_confirmed <dbl> …
## $ previous_day_admission_influenza_confirmed_coverage <dbl> …
## $ previous_day_deaths_covid_and_influenza <dbl> …
## $ previous_day_deaths_covid_and_influenza_coverage <dbl> …
## $ previous_day_deaths_influenza <dbl> …
## $ previous_day_deaths_influenza_coverage <dbl> …
## $ total_patients_hospitalized_confirmed_influenza <dbl> …
## $ total_patients_hospitalized_confirmed_influenza_and_covid <dbl> …
## $ total_patients_hospitalized_confirmed_influenza_and_covid_coverage <dbl> …
## $ total_patients_hospitalized_confirmed_influenza_coverage <dbl> …
## $ all_pediatric_inpatient_bed_occupied <dbl> …
## $ all_pediatric_inpatient_bed_occupied_coverage <dbl> …
## $ all_pediatric_inpatient_beds <dbl> …
## $ all_pediatric_inpatient_beds_coverage <dbl> …
## $ previous_day_admission_pediatric_covid_confirmed_0_4 <dbl> …
## $ previous_day_admission_pediatric_covid_confirmed_0_4_coverage <dbl> …
## $ previous_day_admission_pediatric_covid_confirmed_12_17 <dbl> …
## $ previous_day_admission_pediatric_covid_confirmed_12_17_coverage <dbl> …
## $ previous_day_admission_pediatric_covid_confirmed_5_11 <dbl> …
## $ previous_day_admission_pediatric_covid_confirmed_5_11_coverage <dbl> …
## $ previous_day_admission_pediatric_covid_confirmed_unknown <dbl> …
## $ previous_day_admission_pediatric_covid_confirmed_unknown_coverage <dbl> …
## $ staffed_icu_pediatric_patients_confirmed_covid <dbl> …
## $ staffed_icu_pediatric_patients_confirmed_covid_coverage <dbl> …
## $ staffed_pediatric_icu_bed_occupancy <dbl> …
## $ staffed_pediatric_icu_bed_occupancy_coverage <dbl> …
## $ total_staffed_pediatric_icu_beds <dbl> …
## $ total_staffed_pediatric_icu_beds_coverage <dbl> …## Warning: One or more parsing issues, see `problems()` for details## Rows: 36696 Columns: 101
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (2): Date, Location
## dbl (94): MMWR_week, Distributed, Distributed_Janssen, Distributed_Moderna, ...
## lgl (5): Administered_Bivalent, Admin_Bivalent_PFR, Admin_Bivalent_MOD, Dis...
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
##
## *** File has been checked for uniqueness by: state date
##
##
## Checking for similarity of: column names
## In reference but not in current:
## In current but not in reference:
##
## Checking for similarity of: date
## In reference but not in current: 0
## In current but not in reference: 4
##
## Checking for similarity of: state
## In reference but not in current:
## In current but not in reference:
##
##
## ***Differences of at least 1 and at least 1%
##
## [1] date name newValue refValue absDelta pctDelta
## <0 rows> (or 0-length row.names)##
##
## ***Differences of at least 0 and at least 0.1%
##
## [1] state name newValue refValue absDelta pctDelta
## <0 rows> (or 0-length row.names)
##
##
##
## Raw file for vax:
## Rows: 36,696
## Columns: 101
## $ date <date> 2022-10-26, 2022-10-26, 2022-1…
## $ MMWR_week <dbl> 43, 43, 43, 43, 43, 43, 43, 43,…
## $ state <chr> "CO", "BP2", "DD2", "AR", "LA",…
## $ Distributed <dbl> 15839015, 389930, 7382310, 7633…
## $ Distributed_Janssen <dbl> 500100, 16200, 210700, 262300, …
## $ Distributed_Moderna <dbl> 5607840, 171620, 2394540, 31571…
## $ Distributed_Pfizer <dbl> 9713375, 202110, 4748270, 41899…
## $ Distributed_Novavax <dbl> 17700, 0, 28800, 23800, 6600, 4…
## $ Distributed_Unk_Manuf <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
## $ Dist_Per_100K <dbl> 275043, 0, 0, 252938, 206691, 2…
## $ Distributed_Per_100k_5Plus <dbl> 291881, 0, 0, 269787, 221024, 2…
## $ Distributed_Per_100k_12Plus <dbl> 320884, 0, 0, 298492, 244911, 2…
## $ Distributed_Per_100k_18Plus <dbl> 352039, 0, 0, 329350, 269818, 2…
## $ Distributed_Per_100k_65Plus <dbl> 1880200, 0, 0, 1457040, 1296680…
## $ vxa <dbl> 12152626, 335497, 9079453, 4641…
## $ Administered_5Plus <dbl> 12068391, 335495, 9063024, 4627…
## $ Administered_12Plus <dbl> 11620758, 335495, 8883529, 4499…
## $ Administered_18Plus <dbl> 10883886, 335495, 8387809, 4225…
## $ Administered_65Plus <dbl> 2933378, 11277, 857771, 1405163…
## $ Administered_Janssen <dbl> 345410, 14176, 327958, 125793, …
## $ Administered_Moderna <dbl> 4622790, 154826, 2958844, 19766…
## $ Administered_Pfizer <dbl> 7165826, 166463, 5744807, 25343…
## $ Administered_Novavax <dbl> 1712, 0, 2484, 268, 480, 295, 2…
## $ Administered_Unk_Manuf <dbl> 16888, 32, 45360, 4366, 3639, 5…
## $ Admin_Per_100k <dbl> 211029, 0, 0, 153800, 144276, 1…
## $ Admin_Per_100k_5Plus <dbl> 222396, 0, 0, 163556, 154002, 1…
## $ Admin_Per_100k_12Plus <dbl> 235426, 0, 0, 175954, 167022, 1…
## $ Admin_Per_100k_18Plus <dbl> 241906, 0, 0, 182340, 174300, 2…
## $ Admin_Per_100k_65Plus <dbl> 348212, 0, 0, 268221, 266760, 2…
## $ Recip_Administered <dbl> 12178883, 335497, 9079453, 4695…
## $ Administered_Dose1_Recip <dbl> 4745768, 152116, 4600027, 20805…
## $ Administered_Dose1_Pop_Pct <dbl> 82.4, 0.0, 0.0, 68.9, 62.4, 77.…
## $ Administered_Dose1_Recip_5Plus <dbl> 4700168, 152114, 4589610, 20718…
## $ Administered_Dose1_Recip_5PlusPop_Pct <dbl> 86.6, 0.0, 0.0, 73.2, 66.5, 82.…
## $ Administered_Dose1_Recip_12Plus <dbl> 4480951, 152114, 4486081, 20017…
## $ Administered_Dose1_Recip_12PlusPop_Pct <dbl> 90.8, 0.0, 0.0, 78.3, 71.7, 88.…
## $ Administered_Dose1_Recip_18Plus <dbl> 4155669, 152114, 4230192, 18630…
## $ Administered_Dose1_Recip_18PlusPop_Pct <dbl> 92.4, 0.0, 0.0, 80.4, 74.0, 90.…
## $ Administered_Dose1_Recip_65Plus <dbl> 890491, 4115, 476135, 515480, 6…
## $ Administered_Dose1_Recip_65PlusPop_Pct <dbl> 95.0, 0.0, 0.0, 95.0, 92.3, 95.…
## $ vxc <dbl> 4175718, 138953, 3655064, 16955…
## $ vxcpoppct <dbl> 72.5, 0.0, 0.0, 56.2, 54.7, 62.…
## $ Series_Complete_5Plus <dbl> 4156753, 138952, 3652831, 16926…
## $ Series_Complete_5PlusPop_Pct <dbl> 76.6, 0.0, 0.0, 59.8, 58.4, 66.…
## $ Series_Complete_12Plus <dbl> 3972620, 138952, 3583964, 16411…
## $ Series_Complete_12PlusPop_Pct <dbl> 80.5, 0.0, 0.0, 64.2, 63.2, 71.…
## $ vxcgte18 <dbl> 3681706, 138952, 3383916, 15285…
## $ vxcgte18pct <dbl> 81.8, 0.0, 0.0, 66.0, 65.6, 73.…
## $ vxcgte65 <dbl> 808256, 3763, 251475, 435109, 6…
## $ vxcgte65pct <dbl> 95.0, 0.0, 0.0, 83.1, 86.9, 87.…
## $ Series_Complete_Janssen <dbl> 316583, 13495, 319863, 115591, …
## $ Series_Complete_Moderna <dbl> 1482213, 56214, 1106217, 684277…
## $ Series_Complete_Pfizer <dbl> 2364703, 69244, 2216621, 893449…
## $ Series_Complete_Novavax <dbl> 593, 0, 931, 150, 147, 104, 130…
## $ Series_Complete_Unk_Manuf <dbl> 4720, 0, 10757, 1304, 1348, 186…
## $ Series_Complete_Janssen_5Plus <dbl> 316563, 13494, 319842, 115470, …
## $ Series_Complete_Moderna_5Plus <dbl> 1470443, 56214, 1104733, 683178…
## $ Series_Complete_Pfizer_5Plus <dbl> 2364457, 69244, 2216589, 892657…
## $ Series_Complete_Unk_Manuf_5Plus <dbl> 4699, 0, 10736, 1295, 1343, 186…
## $ Series_Complete_Janssen_12Plus <dbl> 316553, 13494, 319830, 115462, …
## $ Series_Complete_Moderna_12Plus <dbl> 1469786, 56214, 1104614, 682949…
## $ Series_Complete_Pfizer_12Plus <dbl> 2181121, 69244, 2148140, 841426…
## $ Series_Complete_Unk_Manuf_12Plus <dbl> 4569, 0, 10450, 1248, 1306, 183…
## $ Series_Complete_Janssen_18Plus <dbl> 316207, 13494, 319494, 115013, …
## $ Series_Complete_Moderna_18Plus <dbl> 1469043, 56214, 1103520, 681652…
## $ Series_Complete_Pfizer_18Plus <dbl> 1892075, 69244, 1950664, 730710…
## $ Series_Complete_Unk_Manuf_18Plus <dbl> 3801, 0, 9316, 1173, 1259, 176,…
## $ Series_Complete_Janssen_65Plus <dbl> 29249, 196, 10615, 15940, 22522…
## $ Series_Complete_Moderna_65Plus <dbl> 383852, 1826, 84801, 243252, 30…
## $ Series_Complete_Pfizer_65Plus <dbl> 393887, 1741, 155484, 175349, 3…
## $ Series_Complete_Unk_Manuf_65Plus <dbl> 1189, 0, 563, 564, 219, 63, 878…
## $ Additional_Doses <dbl> 2342627, 52153, 966851, 730243,…
## $ Additional_Doses_Vax_Pct <dbl> 56.1, 37.5, 26.5, 43.1, 42.3, 4…
## $ Additional_Doses_5Plus <dbl> 2342527, 52153, 966774, 730158,…
## $ Additional_Doses_5Plus_Vax_Pct <dbl> 56.4, 37.5, 26.5, 43.1, 42.3, 4…
## $ Additional_Doses_12Plus <dbl> 2300475, 52153, 959937, 723815,…
## $ Additional_Doses_12Plus_Vax_Pct <dbl> 57.9, 37.5, 26.8, 44.1, 43.1, 4…
## $ Additional_Doses_18Plus <dbl> 2188216, 52153, 921716, 700345,…
## $ Additional_Doses_18Plus_Vax_Pct <dbl> 59.4, 37.5, 27.2, 45.8, 44.8, 4…
## $ Additional_Doses_50Plus <dbl> 1227186, 16410, 283327, 492070,…
## $ Additional_Doses_50Plus_Vax_Pct <dbl> 72.5, 54.6, 38.0, 58.5, 58.2, 5…
## $ Additional_Doses_65Plus <dbl> 645598, 2658, 100062, 298265, 4…
## $ Additional_Doses_65Plus_Vax_Pct <dbl> 79.9, 70.6, 39.8, 68.5, 68.3, 6…
## $ Additional_Doses_Moderna <dbl> 989679, 31561, 327343, 345696, …
## $ Additional_Doses_Pfizer <dbl> 1323953, 19927, 628820, 373658,…
## $ Additional_Doses_Janssen <dbl> 27100, 643, 6220, 10264, 13964,…
## $ Additional_Doses_Unk_Manuf <dbl> 1855, 22, 4183, 611, 260, 55, 3…
## $ Second_Booster <dbl> NA, NA, NA, NA, NA, NA, NA, NA,…
## $ Second_Booster_50Plus <dbl> 652497, 3571, 74708, 190165, 24…
## $ Second_Booster_50Plus_Vax_Pct <dbl> 53.2, 21.8, 26.4, 38.6, 32.1, 3…
## $ Second_Booster_65Plus <dbl> 400749, 866, 33346, 135359, 166…
## $ Second_Booster_65Plus_Vax_Pct <dbl> 62.1, 32.6, 33.3, 45.4, 37.9, 4…
## $ Second_Booster_Janssen <dbl> 421, 3, 94, 116, 168, 53, 313, …
## $ Second_Booster_Moderna <dbl> 325593, 2532, 30870, 104368, 11…
## $ Second_Booster_Pfizer <dbl> 493405, 2152, 74296, 108959, 15…
## $ Second_Booster_Unk_Manuf <dbl> 742, 2, 595, 149, 38, 17, 67, 0…
## $ Administered_Bivalent <lgl> NA, NA, NA, NA, NA, NA, NA, NA,…
## $ Admin_Bivalent_PFR <lgl> NA, NA, NA, NA, NA, NA, NA, NA,…
## $ Admin_Bivalent_MOD <lgl> NA, NA, NA, NA, NA, NA, NA, NA,…
## $ Dist_Bivalent_PFR <lgl> NA, NA, NA, NA, NA, NA, NA, NA,…
## $ Dist_Bivalent_MOD <lgl> NA, NA, NA, NA, NA, NA, NA, NA,…
##
## Column sums before and after applying filtering rules:
## # A tibble: 3 × 6
## isType tot_cases tot_deaths new_cases new_deaths n
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 before 3.95e+10 5.62e+8 96180659 1042962 59059
## 2 after 3.92e+10 5.59e+8 95065282 1037080 51051
## 3 pctchg 7.28e- 3 4.64e-3 0.0116 0.00564 0.136
##
##
## Processed for cdcDaily:
## Rows: 51,051
## Columns: 6
## $ date <date> 2021-03-11, 2021-12-01, 2021-11-22, 2022-05-30, 2020-04-03…
## $ state <chr> "KS", "ND", "AL", "AK", "ND", "NM", "UT", "SD", "OH", "OK",…
## $ tot_cases <dbl> 297229, 163565, 841461, 251425, 173, 602931, 636992, 122688…
## $ tot_deaths <dbl> 4851, 1907, 16377, 1252, 3, 8318, 3787, 1967, 18646, 471, 1…
## $ new_cases <dbl> 0, 589, 703, 0, 14, 1509, 0, 28, 2293, 1040, 133, 133, 1028…
## $ new_deaths <dbl> 0, 9, 7, 0, 0, 6, 0, 0, 0, 4, 0, 14, 8, 0, 73, 3, 0, 0, 7, …
##
## Column sums before and after applying filtering rules:
## # A tibble: 3 × 5
## isType inp hosp_adult hosp_ped n
## <chr> <dbl> <dbl> <dbl> <dbl>
## 1 before 5.27e+7 4.60e+7 1311468 52391
## 2 after 5.25e+7 4.58e+7 1285571 50037
## 3 pctchg 5.43e-3 5.19e-3 0.0197 0.0449
##
##
## Processed for cdcHosp:
## Rows: 50,037
## Columns: 5
## $ date <date> 2021-01-26, 2021-01-23, 2021-01-21, 2021-01-13, 2021-01-10…
## $ state <chr> "OK", "MN", "RI", "OR", "RI", "KS", "SC", "RI", "RI", "LA",…
## $ inp <dbl> 1598, 618, 407, 512, 449, 989, 2246, 461, 483, 1619, 471, 7…
## $ hosp_adult <dbl> 1516, 598, 399, 507, 446, 986, 2232, 456, 479, 1609, 469, 7…
## $ hosp_ped <dbl> 82, 20, 8, 5, 3, 3, 14, 5, 4, 10, 2, 81, 16, 1, 9, 22, 1, 0…
##
## Column sums before and after applying filtering rules:
## # A tibble: 3 × 9
## isType vxa vxc vxcpoppct vxcgte65 vxcgt…¹ vxcgte18 vxcgt…² n
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 before 4.24e+11 1.74e+11 1544457. 4.40e+10 2.28e+6 1.61e+11 1.82e+6 3.67e+4
## 2 after 2.04e+11 8.40e+10 1292563. 2.13e+10 2.02e+6 7.77e+10 1.54e+6 2.90e+4
## 3 pctchg 5.18e- 1 5.16e- 1 0.163 5.16e- 1 1.15e-1 5.16e- 1 1.54e-1 2.09e-1
## # … with abbreviated variable names ¹vxcgte65pct, ²vxcgte18pct
##
##
## Processed for vax:
## Rows: 29,019
## Columns: 9
## $ date <date> 2022-10-26, 2022-10-26, 2022-10-26, 2022-10-26, 2022-10-2…
## $ state <chr> "CO", "AR", "LA", "NV", "KY", "MN", "AL", "OH", "MT", "WV"…
## $ vxa <dbl> 12152626, 4641392, 6707088, 5315414, 7144109, 11838868, 66…
## $ vxc <dbl> 4175718, 1695580, 2541814, 1930260, 2634385, 4013235, 2576…
## $ vxcpoppct <dbl> 72.5, 56.2, 54.7, 62.7, 59.0, 71.2, 52.5, 59.8, 58.4, 59.2…
## $ vxcgte65 <dbl> 808256, 435109, 644253, 434579, 663968, 908701, 714593, 18…
## $ vxcgte65pct <dbl> 95.0, 83.1, 86.9, 87.6, 88.5, 95.0, 84.1, 88.9, 88.3, 86.8…
## $ vxcgte18 <dbl> 3681706, 1528584, 2334821, 1747560, 2393411, 3494198, 2386…
## $ vxcgte18pct <dbl> 81.8, 66.0, 65.6, 73.2, 69.1, 80.6, 62.6, 69.2, 67.6, 68.4…
##
## Integrated per capita data file:
## Rows: 51,927
## Columns: 34
## $ date <date> 2020-01-01, 2020-01-01, 2020-01-01, 2020-01-01, 2020-01-0…
## $ state <chr> "AL", "HI", "IN", "LA", "MN", "MT", "NC", "TX", "AL", "HI"…
## $ tot_cases <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ tot_deaths <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ new_cases <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ new_deaths <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ inp <dbl> NA, 0, 0, NA, 0, 0, 0, 0, NA, 0, 0, NA, 0, 0, 0, 1877, 0, …
## $ hosp_adult <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ hosp_ped <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ vxa <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ vxc <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ vxcpoppct <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ vxcgte65 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ vxcgte65pct <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ vxcgte18 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ vxcgte18pct <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ tcpm <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ tdpm <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ cpm <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ dpm <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ hpm <dbl> NA, 0.0000, 0.0000, NA, 0.0000, 0.0000, 0.0000, 0.0000, NA…
## $ ahpm <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ phpm <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ vxapm <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ vxcpm <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ tcpm7 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ tdpm7 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ cpm7 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ dpm7 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ hpm7 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ ahpm7 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ phpm7 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ vxapm7 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ vxcpm7 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…## Warning in showSRID(uprojargs, format = "PROJ", multiline = "NO", prefer_proj =
## prefer_proj): Discarded datum unknown in Proj4 definition
saveToRDS(cdc_daily_221102, ovrWriteError=FALSE)The CDC modified deaths and cases reporting to be weekly, based on summation of county-level data to states. Per their website, methodology has changed (inconsistency between datasets?) and the previous daily data is no longer updated as of October 20, 2022. Weekly deaths and cases data are available at CDC website
The latest hospitalization data is also downloaded and processed:
# Run for latest data, save as RDS
indivHosp_20221103 <- downloadReadHospitalData(loc="./RInputFiles/Coronavirus/HHS_Hospital_20221103.csv")##
## File ./RInputFiles/Coronavirus/HHS_Hospital_20221103.csv already exists
## File will not be downloaded since ovrWrite is not TRUE## Rows: 132411 Columns: 128
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (11): hospital_pk, state, ccn, hospital_name, address, city, zip, hosp...
## dbl (114): total_beds_7_day_avg, all_adult_hospital_beds_7_day_avg, all_adu...
## lgl (2): is_metro_micro, is_corrected
## date (1): collection_week
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.## Rows: 132,411
## Columns: 128
## $ hospital_pk <chr> …
## $ collection_week <date> …
## $ state <chr> …
## $ ccn <chr> …
## $ hospital_name <chr> …
## $ address <chr> …
## $ city <chr> …
## $ zip <chr> …
## $ hospital_subtype <chr> …
## $ fips_code <chr> …
## $ is_metro_micro <lgl> …
## $ total_beds_7_day_avg <dbl> …
## $ all_adult_hospital_beds_7_day_avg <dbl> …
## $ all_adult_hospital_inpatient_beds_7_day_avg <dbl> …
## $ inpatient_beds_used_7_day_avg <dbl> …
## $ all_adult_hospital_inpatient_bed_occupied_7_day_avg <dbl> …
## $ inpatient_beds_used_covid_7_day_avg <dbl> …
## $ total_adult_patients_hospitalized_confirmed_and_suspected_covid_7_day_avg <dbl> …
## $ total_adult_patients_hospitalized_confirmed_covid_7_day_avg <dbl> …
## $ total_pediatric_patients_hospitalized_confirmed_and_suspected_covid_7_day_avg <dbl> …
## $ total_pediatric_patients_hospitalized_confirmed_covid_7_day_avg <dbl> …
## $ inpatient_beds_7_day_avg <dbl> …
## $ total_icu_beds_7_day_avg <dbl> …
## $ total_staffed_adult_icu_beds_7_day_avg <dbl> …
## $ icu_beds_used_7_day_avg <dbl> …
## $ staffed_adult_icu_bed_occupancy_7_day_avg <dbl> …
## $ staffed_icu_adult_patients_confirmed_and_suspected_covid_7_day_avg <dbl> …
## $ staffed_icu_adult_patients_confirmed_covid_7_day_avg <dbl> …
## $ total_patients_hospitalized_confirmed_influenza_7_day_avg <dbl> …
## $ icu_patients_confirmed_influenza_7_day_avg <dbl> …
## $ total_patients_hospitalized_confirmed_influenza_and_covid_7_day_avg <dbl> …
## $ total_beds_7_day_sum <dbl> …
## $ all_adult_hospital_beds_7_day_sum <dbl> …
## $ all_adult_hospital_inpatient_beds_7_day_sum <dbl> …
## $ inpatient_beds_used_7_day_sum <dbl> …
## $ all_adult_hospital_inpatient_bed_occupied_7_day_sum <dbl> …
## $ inpatient_beds_used_covid_7_day_sum <dbl> …
## $ total_adult_patients_hospitalized_confirmed_and_suspected_covid_7_day_sum <dbl> …
## $ total_adult_patients_hospitalized_confirmed_covid_7_day_sum <dbl> …
## $ total_pediatric_patients_hospitalized_confirmed_and_suspected_covid_7_day_sum <dbl> …
## $ total_pediatric_patients_hospitalized_confirmed_covid_7_day_sum <dbl> …
## $ inpatient_beds_7_day_sum <dbl> …
## $ total_icu_beds_7_day_sum <dbl> …
## $ total_staffed_adult_icu_beds_7_day_sum <dbl> …
## $ icu_beds_used_7_day_sum <dbl> …
## $ staffed_adult_icu_bed_occupancy_7_day_sum <dbl> …
## $ staffed_icu_adult_patients_confirmed_and_suspected_covid_7_day_sum <dbl> …
## $ staffed_icu_adult_patients_confirmed_covid_7_day_sum <dbl> …
## $ total_patients_hospitalized_confirmed_influenza_7_day_sum <dbl> …
## $ icu_patients_confirmed_influenza_7_day_sum <dbl> …
## $ total_patients_hospitalized_confirmed_influenza_and_covid_7_day_sum <dbl> …
## $ total_beds_7_day_coverage <dbl> …
## $ all_adult_hospital_beds_7_day_coverage <dbl> …
## $ all_adult_hospital_inpatient_beds_7_day_coverage <dbl> …
## $ inpatient_beds_used_7_day_coverage <dbl> …
## $ all_adult_hospital_inpatient_bed_occupied_7_day_coverage <dbl> …
## $ inpatient_beds_used_covid_7_day_coverage <dbl> …
## $ total_adult_patients_hospitalized_confirmed_and_suspected_covid_7_day_coverage <dbl> …
## $ total_adult_patients_hospitalized_confirmed_covid_7_day_coverage <dbl> …
## $ total_pediatric_patients_hospitalized_confirmed_and_suspected_covid_7_day_coverage <dbl> …
## $ total_pediatric_patients_hospitalized_confirmed_covid_7_day_coverage <dbl> …
## $ inpatient_beds_7_day_coverage <dbl> …
## $ total_icu_beds_7_day_coverage <dbl> …
## $ total_staffed_adult_icu_beds_7_day_coverage <dbl> …
## $ icu_beds_used_7_day_coverage <dbl> …
## $ staffed_adult_icu_bed_occupancy_7_day_coverage <dbl> …
## $ staffed_icu_adult_patients_confirmed_and_suspected_covid_7_day_coverage <dbl> …
## $ staffed_icu_adult_patients_confirmed_covid_7_day_coverage <dbl> …
## $ total_patients_hospitalized_confirmed_influenza_7_day_coverage <dbl> …
## $ icu_patients_confirmed_influenza_7_day_coverage <dbl> …
## $ total_patients_hospitalized_confirmed_influenza_and_covid_7_day_coverage <dbl> …
## $ previous_day_admission_adult_covid_confirmed_7_day_sum <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_18-19_7_day_sum` <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_20-29_7_day_sum` <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_30-39_7_day_sum` <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_40-49_7_day_sum` <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_50-59_7_day_sum` <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_60-69_7_day_sum` <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_70-79_7_day_sum` <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_80+_7_day_sum` <dbl> …
## $ previous_day_admission_adult_covid_confirmed_unknown_7_day_sum <dbl> …
## $ previous_day_admission_pediatric_covid_confirmed_7_day_sum <dbl> …
## $ previous_day_covid_ED_visits_7_day_sum <dbl> …
## $ previous_day_admission_adult_covid_suspected_7_day_sum <dbl> …
## $ `previous_day_admission_adult_covid_suspected_18-19_7_day_sum` <dbl> …
## $ `previous_day_admission_adult_covid_suspected_20-29_7_day_sum` <dbl> …
## $ `previous_day_admission_adult_covid_suspected_30-39_7_day_sum` <dbl> …
## $ `previous_day_admission_adult_covid_suspected_40-49_7_day_sum` <dbl> …
## $ `previous_day_admission_adult_covid_suspected_50-59_7_day_sum` <dbl> …
## $ `previous_day_admission_adult_covid_suspected_60-69_7_day_sum` <dbl> …
## $ `previous_day_admission_adult_covid_suspected_70-79_7_day_sum` <dbl> …
## $ `previous_day_admission_adult_covid_suspected_80+_7_day_sum` <dbl> …
## $ previous_day_admission_adult_covid_suspected_unknown_7_day_sum <dbl> …
## $ previous_day_admission_pediatric_covid_suspected_7_day_sum <dbl> …
## $ previous_day_total_ED_visits_7_day_sum <dbl> …
## $ previous_day_admission_influenza_confirmed_7_day_sum <dbl> …
## $ geocoded_hospital_address <chr> …
## $ hhs_ids <chr> …
## $ previous_day_admission_adult_covid_confirmed_7_day_coverage <dbl> …
## $ previous_day_admission_pediatric_covid_confirmed_7_day_coverage <dbl> …
## $ previous_day_admission_adult_covid_suspected_7_day_coverage <dbl> …
## $ previous_day_admission_pediatric_covid_suspected_7_day_coverage <dbl> …
## $ previous_week_personnel_covid_vaccinated_doses_administered_7_day <dbl> …
## $ total_personnel_covid_vaccinated_doses_none_7_day <dbl> …
## $ total_personnel_covid_vaccinated_doses_one_7_day <dbl> …
## $ total_personnel_covid_vaccinated_doses_all_7_day <dbl> …
## $ previous_week_patients_covid_vaccinated_doses_one_7_day <dbl> …
## $ previous_week_patients_covid_vaccinated_doses_all_7_day <dbl> …
## $ is_corrected <lgl> …
## $ all_pediatric_inpatient_bed_occupied_7_day_avg <dbl> …
## $ all_pediatric_inpatient_bed_occupied_7_day_coverage <dbl> …
## $ all_pediatric_inpatient_bed_occupied_7_day_sum <dbl> …
## $ all_pediatric_inpatient_beds_7_day_avg <dbl> …
## $ all_pediatric_inpatient_beds_7_day_coverage <dbl> …
## $ all_pediatric_inpatient_beds_7_day_sum <dbl> …
## $ previous_day_admission_pediatric_covid_confirmed_0_4_7_day_sum <dbl> …
## $ previous_day_admission_pediatric_covid_confirmed_12_17_7_day_sum <dbl> …
## $ previous_day_admission_pediatric_covid_confirmed_5_11_7_day_sum <dbl> …
## $ previous_day_admission_pediatric_covid_confirmed_unknown_7_day_sum <dbl> …
## $ staffed_icu_pediatric_patients_confirmed_covid_7_day_avg <dbl> …
## $ staffed_icu_pediatric_patients_confirmed_covid_7_day_coverage <dbl> …
## $ staffed_icu_pediatric_patients_confirmed_covid_7_day_sum <dbl> …
## $ staffed_pediatric_icu_bed_occupancy_7_day_avg <dbl> …
## $ staffed_pediatric_icu_bed_occupancy_7_day_coverage <dbl> …
## $ staffed_pediatric_icu_bed_occupancy_7_day_sum <dbl> …
## $ total_staffed_pediatric_icu_beds_7_day_avg <dbl> …
## $ total_staffed_pediatric_icu_beds_7_day_coverage <dbl> …
## $ total_staffed_pediatric_icu_beds_7_day_sum <dbl> …
##
## Hospital Subtype Counts:
## # A tibble: 4 × 2
## hospital_subtype n
## <chr> <int>
## 1 Childrens Hospitals 2472
## 2 Critical Access Hospitals 35614
## 3 Long Term 8968
## 4 Short Term 85357
##
## Records other than 50 states and DC
## # A tibble: 5 × 2
## state n
## <chr> <int>
## 1 AS 12
## 2 GU 68
## 3 MP 25
## 4 PR 1311
## 5 VI 54
##
## Record types for key metrics
## # A tibble: 10 × 5
## name `NA` Posit…¹ Value…² Total
## <chr> <int> <int> <int> <int>
## 1 all_adult_hospital_beds_7_day_avg 36802 95379 230 132411
## 2 all_adult_hospital_inpatient_bed_occupied_7_day… 20208 102724 9479 132411
## 3 icu_beds_used_7_day_avg 22029 96812 13570 132411
## 4 inpatient_beds_7_day_avg 5519 126362 530 132411
## 5 inpatient_beds_used_7_day_avg 5519 116077 10815 132411
## 6 inpatient_beds_used_covid_7_day_avg 1253 88897 42261 132411
## 7 staffed_icu_adult_patients_confirmed_and_suspec… 20215 77013 35183 132411
## 8 total_adult_patients_hospitalized_confirmed_and… 18629 77473 36309 132411
## 9 total_beds_7_day_avg 20916 111369 126 132411
## 10 total_icu_beds_7_day_avg 2231 123464 6716 132411
## # … with abbreviated variable names ¹Positive, ²`Value -999999`## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
saveToRDS(indivHosp_20221103, ovrWriteError=FALSE)Post-processing is run, including hospital summaries:
# Create pivoted burden data
burdenPivotList_221102 <- postProcessCDCDaily(cdc_daily_221102,
dataThruLabel="Oct 2022",
keyDatesBurden=c("2022-10-15", "2022-04-15",
"2021-10-15", "2021-04-15"
),
keyDatesVaccine=c("2022-10-26", "2022-04-30",
"2021-10-31", "2021-04-30"
),
returnData=TRUE
)## Joining, by = "state"##
## *** File has been checked for uniqueness by: state date name## Warning: Removed 24 row(s) containing missing values (geom_path).
## Warning: Removed 24 rows containing missing values (position_stack).
## Warning: Removed 24 rows containing missing values (position_stack).
## Warning: Removed 9 row(s) containing missing values (geom_path).
# Create hospitalized per capita data
hospPerCap_221102 <- hospAgePerCapita(readFromRDS("dfStateAgeBucket2019"),
lst=burdenPivotList_221102,
popVar="pop2019",
excludeState=c(),
cumStartDate="2020-07-15"
)## Warning: Removed 18 row(s) containing missing values (geom_path).
burdenPivotList_221102$hospAge %>%
group_by(adultPed, confSusp, age, name) %>%
summarize(value=sum(value, na.rm=TRUE), n=n(), .groups="drop")## # A tibble: 18 × 6
## adultPed confSusp age name value n
## <chr> <chr> <chr> <chr> <dbl> <int>
## 1 adult confirmed 0-19 previous_day_admission_adult_covid_con… 4.83e4 52391
## 2 adult confirmed 20-29 previous_day_admission_adult_covid_con… 2.95e5 52391
## 3 adult confirmed 30-39 previous_day_admission_adult_covid_con… 4.25e5 52391
## 4 adult confirmed 40-49 previous_day_admission_adult_covid_con… 5.07e5 52391
## 5 adult confirmed 50-59 previous_day_admission_adult_covid_con… 8.11e5 52391
## 6 adult confirmed 60-69 previous_day_admission_adult_covid_con… 1.07e6 52391
## 7 adult confirmed 70-79 previous_day_admission_adult_covid_con… 1.09e6 52391
## 8 adult confirmed 80+ previous_day_admission_adult_covid_con… 9.88e5 52391
## 9 adult suspected 0-19 previous_day_admission_adult_covid_sus… 4.00e4 52391
## 10 adult suspected 20-29 previous_day_admission_adult_covid_sus… 2.67e5 52391
## 11 adult suspected 30-39 previous_day_admission_adult_covid_sus… 3.51e5 52391
## 12 adult suspected 40-49 previous_day_admission_adult_covid_sus… 3.55e5 52391
## 13 adult suspected 50-59 previous_day_admission_adult_covid_sus… 5.61e5 52391
## 14 adult suspected 60-69 previous_day_admission_adult_covid_sus… 7.75e5 52391
## 15 adult suspected 70-79 previous_day_admission_adult_covid_sus… 7.57e5 52391
## 16 adult suspected 80+ previous_day_admission_adult_covid_sus… 6.90e5 52391
## 17 ped confirmed 0-19 previous_day_admission_pediatric_covid… 1.77e5 52391
## 18 ped suspected 0-19 previous_day_admission_pediatric_covid… 4.04e5 52391saveToRDS(burdenPivotList_221102, ovrWriteError=FALSE)
saveToRDS(hospPerCap_221102, ovrWriteError=FALSE)Peaks and valleys of key metrics are also updated:
peakValleyCDCDaily(cdc_daily_221102)## Warning: Removed 6 row(s) containing missing values (geom_path).
## Warning: Removed 6 row(s) containing missing values (geom_path).
## Warning: Removed 6 row(s) containing missing values (geom_path).
## Warning: Removed 20 row(s) containing missing values (geom_path).
## Warning: Removed 20 row(s) containing missing values (geom_path).
## # A tibble: 8,412 × 8
## date state vxa vxc vxa_isPeak vxc_isPeak vxa_isValley vxc_isValley
## <date> <chr> <dbl> <dbl> <lgl> <lgl> <lgl> <lgl>
## 1 2020-12-01 CA NA NA FALSE FALSE FALSE FALSE
## 2 2020-12-01 FL NA NA FALSE FALSE FALSE FALSE
## 3 2020-12-01 GA NA NA FALSE FALSE FALSE FALSE
## 4 2020-12-01 IL NA NA FALSE FALSE FALSE FALSE
## 5 2020-12-01 MI NA NA FALSE FALSE FALSE FALSE
## 6 2020-12-01 NC NA NA FALSE FALSE FALSE FALSE
## 7 2020-12-01 NJ NA NA FALSE FALSE FALSE FALSE
## 8 2020-12-01 NY NA NA FALSE FALSE FALSE FALSE
## 9 2020-12-01 OH NA NA FALSE FALSE FALSE FALSE
## 10 2020-12-01 PA NA NA FALSE FALSE FALSE FALSE
## # … with 8,402 more rows
## # ℹ Use `print(n = ...)` to see more rowsHospital data are pieced together as needed:
# Create modified hospital data
multiSourceHosp_20221102 <- multiSourceDataCombine(list(readFromRDS("indivHosp_20220704"),
readFromRDS("indivHosp_20221103")
),
timeVec=as.Date("2022-01-01")
)The updated hospital data are then plotted:
# Run hospital plots
modStateHosp_20221102 <- hospitalCapacityCDCDaily(multiSourceHosp_20221102,
plotSub="Aug 2020 to Oct 2022\nOld data used pre-2022"
)
An example of the new data file is downloaded manually and then read:
tmpBurden <- fileRead("./RInputFiles/Coronavirus/Weekly_United_States_COVID-19_Cases_and_Deaths_by_State.csv")## Rows: 8820 Columns: 8
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (1): state
## dbl (4): tot_cases, new_cases, tot_deaths, new_deaths
## date (3): date_updated, start_date, end_date
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.glimpse(tmpBurden)## Rows: 8,820
## Columns: 8
## $ date_updated <date> 2020-01-23, 2020-01-30, 2020-02-06, 2020-02-13, 2020-02-…
## $ state <chr> "AK", "AK", "AK", "AK", "AK", "AK", "AK", "AK", "AK", "AK…
## $ start_date <date> 2020-01-16, 2020-01-23, 2020-01-30, 2020-02-06, 2020-02-…
## $ end_date <date> 2020-01-22, 2020-01-29, 2020-02-05, 2020-02-12, 2020-02-…
## $ tot_cases <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 11, 63, 149, 235, 300, 337, 355, …
## $ new_cases <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 11, 52, 86, 86, 65, 37, 18, 19, 1…
## $ tot_deaths <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 3, 7, 9, 9, 9, 10, 10, 10, …
## $ new_deaths <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 4, 2, 0, 0, 1, 0, 0, 0, …# Check for states
tmpStateBurden <- tmpBurden %>%
arrange(end_date, state) %>%
group_by(state) %>%
summarize(across(c(new_cases, new_deaths), .fns=sum, na.rm=TRUE),
across(c(tot_cases, tot_deaths), .fns=max, na.rm=TRUE)
) %>%
ungroup()
# Are all 50 states and DC included?
tmpStateBurden %>%
filter(state %in% c(state.abb, "DC")) %>%
arrange(desc(tot_deaths)) %>%
print(n=+Inf)## # A tibble: 51 × 5
## state new_cases new_deaths tot_cases tot_deaths
## <chr> <dbl> <dbl> <dbl> <dbl>
## 1 CA 11401645 96334 11401645 96334
## 2 TX 8018724 91253 8018724 91253
## 3 FL 7215695 82535 7215695 82535
## 4 PA 3329409 47934 3329409 47934
## 5 GA 2929463 40803 2929463 40803
## 6 OH 3197300 40249 3197300 40249
## 7 IL 3832739 40083 3832739 40083
## 8 MI 2907819 39574 2907819 39574
## 9 NJ 2813645 34958 2813645 34958
## 10 AZ 2300375 31613 2300375 31613
## 11 NY 3329357 30451 3329357 30451
## 12 TN 2371639 28187 2371639 28187
## 13 NC 3255540 27267 3255540 27267
## 14 IN 1948019 25013 1948019 25013
## 15 VA 2129684 22234 2129684 22234
## 16 MA 2099238 22209 2099238 22209
## 17 MO 1684433 21938 1684433 21938
## 18 AL 1540329 20608 1540329 20608
## 19 SC 1729333 18660 1729333 18660
## 20 LA 1465700 18218 1465700 18218
## 21 KY 1619549 17363 1619549 17363
## 22 MD 1275208 15603 1275208 15603
## 23 WI 1907232 15511 1907232 15511
## 24 OK 1211210 14992 1211210 14992
## 25 WA 1843926 14653 1843926 14653
## 26 MN 1694438 13558 1694438 13558
## 27 CO 1683848 13473 1683848 13473
## 28 MS 935770 13005 935770 13005
## 29 AR 962764 12523 962764 12523
## 30 NV 857473 11580 857473 11580
## 31 CT 915786 11527 915786 11527
## 32 IA 866615 10229 866615 10229
## 33 KS 893300 9156 893300 9626
## 34 OR 913809 8743 913809 8743
## 35 NM 633631 8675 633631 8675
## 36 WV 610432 7538 610432 7538
## 37 ID 502130 5237 502130 5237
## 38 UT 1050455 5066 1050455 5066
## 39 NE 537129 4606 537129 4606
## 40 RI 413725 3705 413725 3705
## 41 MT 316251 3581 316251 3581
## 42 DE 314553 3148 314553 3148
## 43 SD 265890 3078 265890 3078
## 44 NH 358819 2782 358819 2782
## 45 ME 299321 2711 299321 2711
## 46 ND 273779 2232 273779 2232
## 47 WY 179366 1917 179366 1917
## 48 HI 351617 1685 351617 1685
## 49 DC 170482 1392 170482 1402
## 50 AK 285355 1376 285355 1376
## 51 VT 145838 763 145838 763# What other states are included?
tmpStateBurden %>%
filter(!(state %in% c(state.abb, "DC"))) %>%
arrange(desc(tot_deaths)) %>%
print(n=+Inf)## # A tibble: 9 × 5
## state new_cases new_deaths tot_cases tot_deaths
## <chr> <dbl> <dbl> <dbl> <dbl>
## 1 NYC 2962479 42409 2962479 42966
## 2 PR 990366 5285 990366 5285
## 3 GU 58990 406 58990 406
## 4 VI 23435 124 23435 124
## 5 FSM 22203 58 22203 58
## 6 MP 13219 41 13219 41
## 7 AS 8257 34 8257 34
## 8 RMI 15386 17 15386 17
## 9 PW 5530 7 5530 7# Are there disconnects between total and sum of new?
tmpStateBurden %>%
filter((new_cases != tot_cases) | (new_deaths != tot_deaths)) %>%
mutate(ratCase=tot_cases/new_cases, ratDeath=tot_deaths/new_deaths)## # A tibble: 3 × 7
## state new_cases new_deaths tot_cases tot_deaths ratCase ratDeath
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 DC 170482 1392 170482 1402 1 1.01
## 2 KS 893300 9156 893300 9626 1 1.05
## 3 NYC 2962479 42409 2962479 42966 1 1.01NYC data appear to be tracked separately from NY data, requiring combination. Otherwise, the expected geographical units appear to be included, and with totals and sum of new matching (exceptions for deaths in DC, NYC, and Kansas).
Similarity of total burden is compared:
tmpBurdenDate <- tmpBurden %>%
select(date=end_date, state, where(is.numeric)) %>%
bind_rows(cdc_daily_221102$dfProcess$cdcDaily, .id="src") %>%
mutate(src=c("1"="CDC weekly (new)", "2"="CDC daily (old)")[src])
tmpBurdenDate## # A tibble: 59,871 × 7
## src date state tot_cases new_cases tot_deaths new_deaths
## <chr> <date> <chr> <dbl> <dbl> <dbl> <dbl>
## 1 CDC weekly (new) 2020-01-22 AK 0 0 0 0
## 2 CDC weekly (new) 2020-01-29 AK 0 0 0 0
## 3 CDC weekly (new) 2020-02-05 AK 0 0 0 0
## 4 CDC weekly (new) 2020-02-12 AK 0 0 0 0
## 5 CDC weekly (new) 2020-02-19 AK 0 0 0 0
## 6 CDC weekly (new) 2020-02-26 AK 0 0 0 0
## 7 CDC weekly (new) 2020-03-04 AK 0 0 0 0
## 8 CDC weekly (new) 2020-03-11 AK 0 0 0 0
## 9 CDC weekly (new) 2020-03-18 AK 11 11 0 0
## 10 CDC weekly (new) 2020-03-25 AK 63 52 1 1
## # … with 59,861 more rows
## # ℹ Use `print(n = ...)` to see more rows# Plot for total deaths and total cases
tmpBurdenDate %>%
filter(state %in% c(state.abb, "DC", "NYC")) %>%
group_by(src, date) %>%
summarize(across(where(is.numeric), sum, na.rm=TRUE), .groups="drop") %>%
pivot_longer(where(is.numeric)) %>%
filter(name %in% c("tot_cases", "tot_deaths")) %>%
ggplot(aes(x=date, y=value)) +
geom_line(aes(color=src, group=src)) +
facet_wrap(~name, scales="free_y")
# Plot by state - latest burden
tmpBurdenDate %>%
filter(state %in% c(state.abb, "DC", "NYC")) %>%
group_by(src, state) %>%
summarize(across(where(is.numeric), last, order_by=date), .groups="drop") %>%
pivot_longer(where(is.numeric)) %>%
filter(name %in% c("tot_cases", "tot_deaths")) %>%
ggplot(aes(x=fct_reorder(state, value), y=value)) +
geom_point(aes(color=src)) +
facet_wrap(~name, scales="free_x") +
coord_flip() +
labs(x=NULL, y=NULL, title="Most recent burden by state and data source") +
scale_color_discrete("Source")
# Plot by state - difference in burden
tmpBurdenDate %>%
filter(state %in% c(state.abb, "DC", "NYC")) %>%
group_by(src, state) %>%
summarize(across(where(is.numeric), last, order_by=date), .groups="drop") %>%
pivot_longer(where(is.numeric)) %>%
filter(name %in% c("tot_cases", "tot_deaths")) %>%
group_by(state, name) %>%
summarize(value=sum(ifelse(src!="CDC daily (old)", value, 0)) - sum(ifelse(src=="CDC daily (old)", value, 0)),
.groups="drop"
) %>%
ggplot(aes(x=fct_reorder(state, value), y=value)) +
geom_point() +
facet_wrap(~name, scales="free_x") +
coord_flip() +
labs(x=NULL, y=NULL, title="Change in most recent burden by state (new minus old)") +
geom_hline(yintercept=0, lty=2)
At a first glance, national totals are well aligned between the existing daily data file and the new weekly data file. The newer data has two weeks of extra reporting, so most recent totals by state are generally slightly higher. The new data breaks apart NYC and NY, which need to be combined for processing the new file
Function readQCRawCDCDaily() is updated:
# Function to read and check a raw data file (last updated 16-NOV-2022, previously updated 02-AUG-2021)
readQCRawCDCDaily <- function(fileName,
writeLog=NULL,
ovrwriteLog=TRUE,
dfRef=NULL,
urlType=NULL,
url=NULL,
getData=TRUE,
ovrWriteDownload=FALSE,
vecRename=NULL,
selfList=NULL,
fullList=NULL,
uniqueBy=NULL,
step3Group=NULL,
step3Vals=NULL,
step4KeyVars=NULL,
step5PlotItems=NULL,
step6AggregateList=NULL,
inferVars=list("url"=urlMapper,
"vecRename"=renMapper,
"selfList"=selfListMapper,
"fullList"=fullListMapper,
"uniqueBy"=uqMapper,
"step3Group"=checkControlGroupMapper,
"step3Vals"=checkControlVarsMapper,
"step4KeyVars"=checkSimilarityMapper,
"step5PlotItems"=plotSimilarityMapper,
"step6AggregateList"=keyAggMapper
)
) {
# FUNCTION ARGUMENTS
# fileName: the location where downloaded data either is, or will be, stored
# writeLog: the external file location for printing (NULL means use the main log stdout)
# ovrwriteLog: boolean, if using an external log, should it be started from scratch (overwritten)?
# dfRef: a reference data frame for comparison (either NULL or NA means do not run comparisons)
# urlType: character vector that can be mapped using urlMapper and keyVarMapper
# url: direct URL passed as character string
# NOTE that if both url and urlType are NULL, no file will be downloaded
# getData: boolean, should an attempt be made to get new data using urlType or url?
# ovrWriteDownload: boolean, if fileName already exists, should it be overwritten?
# vecRename: vector for renaming c('existing name'='new name'), can be any length from 0 to ncol(df)
# NULL means infer from urlType, if not available there use c()
# selfList: list for functions to apply to self, list('variable'=fn) will apply variable=fn(variable)
# processed in order, so more than one function can be applied to self
# NULL means infer from urlType, if not available in mapping file use list()
# fullList: list for general functions to be applied, list('new variable'=expression(code))
# will create 'new variable' as eval(expression(code))
# for now, requires passing an expression
# NULL means infer from urlType, use list() if not in mapping file
# uniqueBy: combination of variables for checking uniqueness
# NULL means infer from data, keep as NULL (meaning use-all) if cannot be inferred
# step3Group: variable to be used as the x-axis (grouping) for step 3 plots
# NULL means infer from data
# step3Vals: values to be plotted on the y-axis for step 3 plots
# NULL means infer from data
# step4KeyVars: list of parameters to be passed as keyVars= in step 4
# NULL means infer from urlType
# step5PlotItems: items to be plotted in step 5
# NULL means infer from urlType
# step6AggregateList: drives the elements to be passed to compareAggregate() and flagLargeDelta()
# NULL means infer from urlType
# inferVars: vector of c('variable'='mapper') for inferring parameter values when passed as NULL
# Step 0a: Use urlType to infer key variables if passed as NULL
for (vrbl in names(inferVars)) {
mapper <- inferVars[[vrbl]]
if (is.null(get(vrbl))) {
if (urlType %in% names(mapper)) assign(vrbl, mapper[[urlType]])
else if ("default" %in% names(mapper)) assign(vrbl, mapper[["default"]])
}
}
# Step 1: Download a new file (if requested)
if (!is.null(url) & isTRUE(getData)) fileDownload(fileName=fileName, url=url, ovrWrite=ovrWriteDownload)
else cat("\nNo file has been downloaded, will use existing file:", fileName, "\n")
# Step 2: Read file, rename and mutate variables, confirm uniqueness by expected levels
dfRaw <- fileRead(fileName) %>%
colRenamer(vecRename) %>%
colMutater(selfList=selfList, fullList=fullList) %>%
checkUniqueRows(uniqueBy=uniqueBy)
# Step 3: Plot basic control totals for new cases and new deaths by month
dfRaw %>%
checkControl(groupBy=step3Group, useVars=step3Vals, printControls=FALSE, na.rm=TRUE) %>%
helperLinePlot(x=step3Group, y="newValue", facetVar="name", facetScales="free_y", groupColor="name")
# If there is no file for comparison, return the data
if (is.null(dfRef) | if(length(dfRef)==1) is.na(dfRef) else FALSE) return(dfRaw)
# Step 4b: Check similarity of existing and reference file
# ovrWriteLog=FALSE since everything should be an append after the opening text line in step 0
diffRaw <- checkSimilarity(df=dfRaw,
ref=dfRef,
keyVars=step4KeyVars,
writeLog=writeLog,
ovrwriteLog=FALSE
)
# Step 5: Plot the similarity checks
plotSimilarity(diffRaw, plotItems=step5PlotItems)
# Step 6: Plot and report on differences in aggregates
helperAggMap <- function(x) {
h1 <- compareAggregate(df=dfRaw, ref=dfRef, grpVar=x$grpVar, numVars=x$numVars,
sameUniverse=x$sameUniverse, plotData=x$plotData, isLine=x$isLine,
returnDelta=x$returnDelta)
if (isTRUE(x$flagLargeDelta)) {
h2 <- flagLargeDelta(h1, pctTol=x$pctTol, absTol=x$absTol, sortBy=x$sortBy,
dropNA=x$dropNA, printAll=x$printAll
)
if (is.null(writeLog)) print(h2)
else {
cat(nrow(h2), " records", sep="")
txt <- paste0("\n\n***Differences of at least ",
x$absTol,
" and at least ",
round(100*x$pctTol, 3), "%\n\n"
)
printLog(h2, txt=txt, writeLog=writeLog)
}
}
}
lapply(step6AggregateList, FUN=helperAggMap)
cat("\n\n")
# Return the raw data file
dfRaw
}
# Explore for only reading and returning new data
testFile <- "./RInputFiles/Coronavirus/Weekly_United_States_COVID-19_Cases_and_Deaths_by_State.csv"
dfTest <- readQCRawCDCDaily(fileName=testFile,
getData=FALSE,
urlType="cdcWeekly",
url=c(),
vecRename=c("end_date"="date"),
selfList=list(),
fullList=list(),
uniqueBy=c("state", "date"),
step3Group=c("date"),
step3Vals=c("new_cases", "new_deaths"),
step4KeyVars=list("date"=list("label"="date", "countOnly"=TRUE, "convChar"=TRUE),
"state"=list("label"="state", "countOnly"=FALSE)
), # will need to update checkSimilarityMapper
step5PlotItems=c("date"),
step6AggregateList=list() # will need to update keyAggMapper
)##
## No file has been downloaded, will use existing file: ./RInputFiles/Coronavirus/Weekly_United_States_COVID-19_Cases_and_Deaths_by_State.csv## Rows: 8820 Columns: 8
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (1): state
## dbl (4): tot_cases, new_cases, tot_deaths, new_deaths
## date (3): date_updated, start_date, end_date
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.##
## *** File has been checked for uniqueness by: state date
all.equal(dfTest, rename(tmpBurden, date=end_date))## [1] TRUEThe function for processing downloaded data, without comparison to previous data, works as intended. Next steps are to update the appropriate mapping files to include “cdcWeeklyBurden” as an option, then check that the function runs as intended. Elements are added to the mapping list manually at first:
# Need to combine NYC as part of NY
lstComboMapper$cdcWeeklyBurden <- list("nyc"=list("comboVar"="state",
"uqVars"="date",
"vecCombo"=c("NY"="NY", "NYC"="NY"),
"fn"=specNA(sum)
)
)
# Need to test URL mapping later
if("cdcWeeklyBurden" %in% names(urlMapper)) {
urlMapper["cdcWeeklyBurden"] <- "https://data.cdc.gov/api/views/pwn4-m3yp/rows.csv?accessType=DOWNLOAD"
} else {
origNames <- names(urlMapper)
urlMapper <- c(urlMapper, "https://data.cdc.gov/api/views/pwn4-m3yp/rows.csv?accessType=DOWNLOAD")
names(urlMapper) <- c(origNames, "cdcWeeklyBurden")
}
# Rename end_date to date
renMapper$cdcWeeklyBurden <- c('end_date'='date')
# No need for variable mapping (formats OK as-is)
selfListMapper$cdcWeeklyBurden <- list()
fullListMapper$cdcWeeklyBurden <- list()
# File should be unique by state-date
uqMapper$cdcWeeklyBurden <- c("state", "date")
# Keep only 50 states (after NYC mapping) and DC
lstFilterMapper$cdcWeeklyBurden <- list("state"=c(state.abb, "DC"))
# Keep date, state, tot_cases, new_cases, tot_deaths, new_deaths
vecSelectMapper$cdcWeeklyBurden <- c("date", "state", "tot_cases", "tot_deaths", "new_cases", "new_deaths")
# Checks for control groups
checkControlGroupMapper$cdcWeeklyBurden <- c("date")
checkControlVarsMapper$cdcWeeklyBurden <- c("new_cases", "new_deaths")
# Check for similarity mapping
checkSimilarityMapper$cdcWeeklyBurden <- list(date=list(label='date', countOnly=TRUE, convChar=TRUE),
state=list(label='state', countOnly=FALSE)
)
plotSimilarityMapper$cdcWeeklyBurden <- c("date")
# Update keyAggMapper (use cdcDaily for now, probably need to update later)
keyAggMapper$cdcWeeklyBurden <- keyAggMapper$cdcDaily
# No variables need to be kept as-is (avoiding rolling 7-day)
asIsMapper$cdcWeeklyBurden <- c()
# No changes needed to perCapMapper or hhsMapper
# Run sample code
dfTest_v2 <- readQCRawCDCDaily(fileName=testFile,
getData=FALSE,
urlType="cdcWeeklyBurden",
url=c()
)##
## No file has been downloaded, will use existing file: ./RInputFiles/Coronavirus/Weekly_United_States_COVID-19_Cases_and_Deaths_by_State.csv## Rows: 8820 Columns: 8
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (1): state
## dbl (4): tot_cases, new_cases, tot_deaths, new_deaths
## date (3): date_updated, start_date, end_date
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.##
## *** File has been checked for uniqueness by: state date
all.equal(dfTest, dfTest_v2)## [1] TRUENext steps are to enable URL downloads and enable checking against a reference file. Downloading of new data is attempted:
# Run sample code
dfTest_v3 <- readQCRawCDCDaily(fileName="./RInputFiles/Coronavirus/CDC_dc_downloaded_221118.csv",
getData=TRUE,
urlType="cdcWeeklyBurden"
)## Rows: 8880 Columns: 10
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (1): state
## dbl (6): tot_cases, new_cases, tot_deaths, new_deaths, new_historic_cases, ...
## date (3): date_updated, start_date, end_date
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.##
## *** File has been checked for uniqueness by: state date
dfTest_v3## # A tibble: 8,880 × 10
## date_updated state start_date date tot_cases new_cases tot_de…¹ new_d…²
## <date> <chr> <date> <date> <dbl> <dbl> <dbl> <dbl>
## 1 2020-01-23 AK 2020-01-16 2020-01-22 0 0 0 0
## 2 2020-01-30 AK 2020-01-23 2020-01-29 0 0 0 0
## 3 2020-02-06 AK 2020-01-30 2020-02-05 0 0 0 0
## 4 2020-02-13 AK 2020-02-06 2020-02-12 0 0 0 0
## 5 2020-02-20 AK 2020-02-13 2020-02-19 0 0 0 0
## 6 2020-02-27 AK 2020-02-20 2020-02-26 0 0 0 0
## 7 2020-03-05 AK 2020-02-27 2020-03-04 0 0 0 0
## 8 2020-03-12 AK 2020-03-05 2020-03-11 0 0 0 0
## 9 2020-03-19 AK 2020-03-12 2020-03-18 11 11 0 0
## 10 2020-03-26 AK 2020-03-19 2020-03-25 63 52 1 1
## # … with 8,870 more rows, 2 more variables: new_historic_cases <dbl>,
## # new_historic_deaths <dbl>, and abbreviated variable names ¹tot_deaths,
## # ²new_deaths
## # ℹ Use `print(n = ...)` to see more rows, and `colnames()` to see all variable namesChecking against a reference file is also enabled:
dfTest_v3_ref <- readQCRawCDCDaily(fileName="./RInputFiles/Coronavirus/CDC_dc_downloaded_221118.csv",
getData=FALSE,
dfRef=dfTest_v2,
urlType="cdcWeeklyBurden"
)##
## No file has been downloaded, will use existing file: ./RInputFiles/Coronavirus/CDC_dc_downloaded_221118.csv## Rows: 8880 Columns: 10
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (1): state
## dbl (6): tot_cases, new_cases, tot_deaths, new_deaths, new_historic_cases, ...
## date (3): date_updated, start_date, end_date
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.##
## *** File has been checked for uniqueness by: state date
##
##
## Checking for similarity of: column names
## In reference but not in current:
## In current but not in reference: new_historic_cases new_historic_deaths
##
## Checking for similarity of: date
## In reference but not in current: 0
## In current but not in reference: 1
##
## Checking for similarity of: state
## In reference but not in current:
## In current but not in reference:
##
##
## ***Differences of at least 5 and at least 5%
##
## date name newValue refValue absDelta pctDelta
## 1 2020-02-26 tot_cases 71 83 12 0.15584416
## 2 2020-03-04 tot_cases 183 201 18 0.09375000
## 3 2020-03-11 tot_cases 1352 1427 75 0.05397625
## 4 2020-02-26 new_cases 24 31 7 0.25454545
## 5 2020-03-04 new_cases 112 118 6 0.05217391
##
##
## ***Differences of at least 0 and at least 0.1%
##
## state name newValue refValue absDelta pctDelta
## 1 FSM tot_cases 206651 206993 342 0.001653596
## 2 PA tot_cases 205201857 205436071 234214 0.001140732
## 3 CO new_deaths 13497 13473 24 0.001779755
## 4 OH new_deaths 40208 40249 41 0.001019178
## 5 FSM new_cases 22032 22203 171 0.007731434
## 6 KY new_cases 1622400 1619549 2851 0.001758819dfTest_v3_ref## # A tibble: 8,880 × 10
## date_updated state start_date date tot_cases new_cases tot_de…¹ new_d…²
## <date> <chr> <date> <date> <dbl> <dbl> <dbl> <dbl>
## 1 2020-01-23 AK 2020-01-16 2020-01-22 0 0 0 0
## 2 2020-01-30 AK 2020-01-23 2020-01-29 0 0 0 0
## 3 2020-02-06 AK 2020-01-30 2020-02-05 0 0 0 0
## 4 2020-02-13 AK 2020-02-06 2020-02-12 0 0 0 0
## 5 2020-02-20 AK 2020-02-13 2020-02-19 0 0 0 0
## 6 2020-02-27 AK 2020-02-20 2020-02-26 0 0 0 0
## 7 2020-03-05 AK 2020-02-27 2020-03-04 0 0 0 0
## 8 2020-03-12 AK 2020-03-05 2020-03-11 0 0 0 0
## 9 2020-03-19 AK 2020-03-12 2020-03-18 11 11 0 0
## 10 2020-03-26 AK 2020-03-19 2020-03-25 63 52 1 1
## # … with 8,870 more rows, 2 more variables: new_historic_cases <dbl>,
## # new_historic_deaths <dbl>, and abbreviated variable names ¹tot_deaths,
## # ²new_deaths
## # ℹ Use `print(n = ...)` to see more rows, and `colnames()` to see all variable namesNew columns have been added to explain jumps in total cases/deaths that are not explained by that week’s reported new cases/deaths. These data are explored:
# Full file
testFull <- read_csv("./RInputFiles/Coronavirus/CDC_dc_downloaded_221118.csv")## Rows: 8880 Columns: 10
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (1): state
## dbl (6): tot_cases, new_cases, tot_deaths, new_deaths, new_historic_cases, ...
## date (3): date_updated, start_date, end_date
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.# Entries with non-zero for "new_historic_*"
testFull %>%
filter(new_historic_cases != 0 | new_historic_deaths != 0)## # A tibble: 5 × 10
## date_upd…¹ state start_date end_date tot_c…² new_c…³ tot_d…⁴ new_d…⁵ new_h…⁶
## <date> <chr> <date> <date> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 2022-10-27 DC 2022-10-20 2022-10-26 169709 273 1392 -5 0
## 2 2022-11-10 DC 2022-11-03 2022-11-09 170482 441 1402 0 0
## 3 2022-09-29 KS 2022-09-22 2022-09-28 879001 2780 9554 7 0
## 4 2022-11-03 KS 2022-10-27 2022-11-02 890598 2388 9620 12 0
## 5 2022-10-27 NYC 2022-10-20 2022-10-26 2928439 14590 42863 91 0
## # … with 1 more variable: new_historic_deaths <dbl>, and abbreviated variable
## # names ¹date_updated, ²tot_cases, ³new_cases, ⁴tot_deaths, ⁵new_deaths,
## # ⁶new_historic_cases
## # ℹ Use `colnames()` to see all variable names# Cases where cumsum and total are not equal
testFull %>%
arrange(state, end_date) %>%
group_by(state) %>%
mutate(delta=tot_deaths-cumsum(new_deaths)) %>%
filter(delta != 0)## # A tibble: 16 × 11
## # Groups: state [3]
## date_updated state start_date end_date tot_cases new_cases tot_de…¹ new_d…²
## <date> <chr> <date> <date> <dbl> <dbl> <dbl> <dbl>
## 1 2022-10-27 DC 2022-10-20 2022-10-26 169709 273 1392 -5
## 2 2022-11-03 DC 2022-10-27 2022-11-02 170041 332 1397 5
## 3 2022-11-10 DC 2022-11-03 2022-11-09 170482 441 1402 0
## 4 2022-11-17 DC 2022-11-10 2022-11-16 170750 268 1403 1
## 5 2022-09-29 KS 2022-09-22 2022-09-28 879001 2780 9554 7
## 6 2022-10-06 KS 2022-09-29 2022-10-05 880633 1632 9573 19
## 7 2022-10-13 KS 2022-10-06 2022-10-12 883482 2849 9590 17
## 8 2022-10-20 KS 2022-10-13 2022-10-19 886123 2641 9601 11
## 9 2022-10-27 KS 2022-10-20 2022-10-26 888210 2087 9607 6
## 10 2022-11-03 KS 2022-10-27 2022-11-02 890598 2388 9620 12
## 11 2022-11-10 KS 2022-11-03 2022-11-09 893300 2702 9626 6
## 12 2022-11-17 KS 2022-11-10 2022-11-16 895776 2476 9652 26
## 13 2022-10-27 NYC 2022-10-20 2022-10-26 2928439 14590 42863 91
## 14 2022-11-03 NYC 2022-10-27 2022-11-02 2944270 15831 42880 17
## 15 2022-11-10 NYC 2022-11-03 2022-11-09 2962479 18209 42957 77
## 16 2022-11-17 NYC 2022-11-10 2022-11-16 2979932 17453 43033 76
## # … with 3 more variables: new_historic_cases <dbl>, new_historic_deaths <dbl>,
## # delta <dbl>, and abbreviated variable names ¹tot_deaths, ²new_deaths
## # ℹ Use `colnames()` to see all variable namesThe field appears to be working as intended. Next steps are to compare the cases and deaths data to the previous daily data files:
# Conversion of raw CDC daily data to cumsum
cdcDailyTest <- cdc_daily_221102$dfRaw$cdcDaily %>%
select(date, state, new_cases, new_deaths) %>%
arrange(state, date) %>%
group_by(state) %>%
mutate(cum_cases=cumsum(new_cases), cum_deaths=cumsum(new_deaths)) %>%
ungroup()
# Integration of new weekly data, using end_date=date
testCombo <- testFull %>%
select(date=end_date, state, wkly_tot_cases=tot_cases, wkly_tot_deaths=tot_deaths) %>%
left_join(cdcDailyTest, by=c("date", "state"))
testCombo## # A tibble: 8,880 × 8
## date state wkly_tot_cases wkly_tot_de…¹ new_c…² new_d…³ cum_c…⁴ cum_d…⁵
## <date> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 2020-01-22 AK 0 0 0 0 0 0
## 2 2020-01-29 AK 0 0 0 0 0 0
## 3 2020-02-05 AK 0 0 0 0 0 0
## 4 2020-02-12 AK 0 0 0 0 0 0
## 5 2020-02-19 AK 0 0 0 0 0 0
## 6 2020-02-26 AK 0 0 0 0 0 0
## 7 2020-03-04 AK 0 0 0 0 0 0
## 8 2020-03-11 AK 0 0 0 0 0 0
## 9 2020-03-18 AK 11 0 5 0 8 1
## 10 2020-03-25 AK 63 1 7 0 49 1
## # … with 8,870 more rows, and abbreviated variable names ¹wkly_tot_deaths,
## # ²new_cases, ³new_deaths, ⁴cum_cases, ⁵cum_deaths
## # ℹ Use `print(n = ...)` to see more rows# Calculation of RMSE by state for 'tot_deaths'
testCombo %>%
filter(date <= "2022-10-12") %>%
mutate(delta=wkly_tot_deaths-cum_deaths) %>%
group_by(state) %>%
summarize(rmse=sqrt(mean(delta**2)), r2=1-rmse/mean(cum_deaths)) %>%
filter(state %in% c(state.abb, "DC", "NYC")) %>%
ggplot(aes(x=fct_reorder(state, r2), y=r2)) +
geom_col(fill="lightblue") +
geom_text(aes(label=round(r2, 2)), hjust=0) +
coord_flip() +
labs(x=NULL, y="Similarity of reported deaths (weekly vs. daily)\n(1.0 means perfect alignment)")
# Select states plotted
testCombo %>%
filter(state %in% c("OR", "LA", "DC", "PA", "NE", "WA", "OH", "MO", "AK")) %>%
select(date, state, wkly_tot_deaths, cum_deaths) %>%
pivot_longer(-c(state, date)) %>%
ggplot(aes(x=date, y=value)) +
geom_line(aes(group=name,
color=c("wkly_tot_deaths"="Weekly data (new)", "cum_deaths"="Daily data (old)")[name]
)
) +
facet_wrap(~state, scales="free_y") +
scale_color_discrete("Source:") +
labs(title="Comparison of cumulative deaths by source", y="Cumulative deaths", x=NULL)## Warning: Removed 5 row(s) containing missing values (geom_path).
# Overall data
testCombo %>%
filter(state %in% c(state.abb, "DC", "NYC")) %>%
group_by(date) %>%
summarize(across(where(is.numeric), specNA())) %>%
select(date, wkly_tot_deaths, cum_deaths) %>%
pivot_longer(-c(date)) %>%
ggplot(aes(x=date, y=value)) +
geom_line(aes(group=name,
color=c("wkly_tot_deaths"="Weekly data (new)", "cum_deaths"="Daily data (old)")[name]
)
) +
scale_color_discrete("Source:") +
labs(title="Comparison of cumulative deaths by source",
y="Cumulative deaths",
x=NULL,
subtitle="50 states plus DC"
)## Warning: Removed 5 row(s) containing missing values (geom_path).
Alignment of reported deaths varies by state. In aggregate, the newer data appears to report slightly more deaths. The data file is processed using the existing function:
dfTestProcess <- processRawFile(dfTest_v3_ref,
vecRename=c(),
vecSelect=vecSelectMapper[["cdcWeeklyBurden"]],
lstCombo=lstComboMapper[["cdcWeeklyBurden"]],
lstFilter=lstFilterMapper[["cdcWeeklyBurden"]]
)##
## Column sums before and after applying filtering rules:
## # A tibble: 3 × 6
## isType tot_cases tot_deaths new_cases new_deaths n
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 before 6.09e+9 8.48e+7 98174364 1072078 8732
## 2 after 6.04e+9 8.44e+7 97030505 1066086 7548
## 3 pctchg 7.56e-3 4.72e-3 0.0117 0.00559 0.136dfTestProcess## # A tibble: 7,548 × 6
## date state tot_cases tot_deaths new_cases new_deaths
## <date> <chr> <dbl> <dbl> <dbl> <dbl>
## 1 2020-01-22 AK 0 0 0 0
## 2 2020-01-29 AK 0 0 0 0
## 3 2020-02-05 AK 0 0 0 0
## 4 2020-02-12 AK 0 0 0 0
## 5 2020-02-19 AK 0 0 0 0
## 6 2020-02-26 AK 0 0 0 0
## 7 2020-03-04 AK 0 0 0 0
## 8 2020-03-11 AK 0 0 0 0
## 9 2020-03-18 AK 11 0 11 0
## 10 2020-03-25 AK 63 1 52 1
## # … with 7,538 more rows
## # ℹ Use `print(n = ...)` to see more rows